The Global SDG Landscape: Performance, Momentum, and Structural Links

Course: 158.888 – Information Technology Professional Project
Student Name: Jesus Eric M. Seacor
Student ID: 2407226
Semester: Semester 2, 2025
Institution: Massey University

Introduction

This notebook explores links among the seventeen Sustainable Development Goals. We gather goal and target lists from the UN feed and join them with ArcGIS indicator data and country scores. We clean codes and titles, build tidy frames, and study patterns across countries and across years. The project follows the wedding cake view from Stockholm Resilience Centre. It places the environment as the base layer that supports society and the economy, with partnerships helping progress across all layers. The article uses the word biosphere. In this notebook we use the word environment for the same idea.

Data Sources

• United Nations SDG API for goal and target lists
• ArcGIS Sustainable Development data for indicators and time series
• SDG Index or other public files for country scores when needed

SDG dimensions (Wedding Cake Model)

Environment

• Goal 6 Clean Water and Sanitation
• Goal 13 Climate Action
• Goal 14 Life Below Water
• Goal 15 Life on Land

Society

• Goal 1 No Poverty
• Goal 2 Zero Hunger
• Goal 3 Good Health and Well being
• Goal 4 Quality Education
• Goal 5 Gender Equality
• Goal 7 Affordable and Clean Energy
• Goal 11 Sustainable Cities and Communities
• Goal 16 Peace Justice and Strong Institutions

Economy

• Goal 8 Decent Work and Economic Growth
• Goal 9 Industry Innovation and Infrastructure
• Goal 10 Reduced Inequalities
• Goal 12 Responsible Consumption and Production

Partnerships

• Goal 17 Partnerships for the Goals

Executive summary

We build one map of codes and titles for goals and indicators and merge it with yearly country scores. We compute and plot correlation heatmaps by year and by region, then group goals into the three broad dimensions to compare cross dimension links. We report the strongest positive and negative pairs, long run trends by region, and the key indicators most tied to low scores.

import pandas as pd
import re
import plotly.io as pio
pio.renderers.default = "notebook"

# ============= Project Kit Module ==============
import importlib
import sdg_mod
importlib.reload(sdg_mod)

from sdg_mod import ProjectKit
sdg = ProjectKit()
# ===============================================

Kaggle

import os

key = None from kaggle_secrets import UserSecretsClient key = UserSecretsClient().get_secret("OPENAI_API_KEY")

os.environ["OPENAI_API_KEY"] = key

from openai import OpenAI client = OpenAI(api_key=key)

import importlib import sys sys.path.append('/kaggle/input/sdg-module') import sdg_module importlib.reload(sdg_module)

from sdg_module import ProjectKit # must come AFTER reload am = ActivityManager()

Data Acquisition

The foundation of this project rests on gathering reliable and diverse sources of SDG data. Since the Sustainable Development Goals are tracked across many indicators, years, and regions, no single dataset is enough. Instead, several complementary sources were brought together to capture both the historical context and the most recent updates.

In this section, I focus on retrieving data from two main pipelines: the UN SDG ArcGIS FeatureServer and the Sustainable Development Report (SDR). The ArcGIS source provides structured data on goals, indicators, and metadata, while the SDR files supply both backdated historical values and the latest 2025 scores. Together, these datasets create a full picture that spans from the year 2000 through 2025.

The purpose of this section is to document where the data came from, what each dataset represents, and how it will feed into the later stages of wrangling and analysis. By clearly outlining the sources and their structure, the project ensures transparency and sets the stage for consistent integration across different SDG measures.

Goals Dataset (UN Stats)

I created the DataFrame df_goals by calling the function get_sdg_goals_grouped().
This function retrieves the list of Sustainable Development Goals (SDGs) from the UN SDG API (https://unstats.un.org/SDGAPI/v1/sdg/Goal/List) and loads them into a structured DataFrame.

The grouping logic is applied directly inside the function, where codes are mapped into categories. For example, codes 1, 2, 3, 4, 5, 7, 11, and 16 are tagged as social, while codes 8, 9, 10, and 12 fall under economic.

At this stage, df_goals simply provides the structured list of goals along with their categories. This is only the initial load before moving into data wrangling and analysis.

df_goals = sdg.get_sdg_goals_grouped(class_code=1) # using 'Wedding Cake' model
print(df_goals.info())
df_goals.to_csv("df_goals.csv", index=False)
df_goals.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17 entries, 0 to 16
Data columns (total 4 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   code         17 non-null     object
 1   title        17 non-null     object
 2   description  17 non-null     object
 3   group        17 non-null     object
dtypes: object(4)
memory usage: 676.0+ bytes
None

	code	title	description	group
0	1	No Poverty	Goal 1 calls for an end to poverty in all its ...	social
1	2	Zero Hunger	Goal 2 seeks to end hunger and all forms of ma...	social
2	3	Good Health and Well-being	Goal 3 aims to ensure health and well-being fo...	social
3	4	Quality Education	Goal 4 focuses on the acquisition of foundatio...	social
4	5	Gender Equality	Goal 5 aims to empower women and girls to reac...	social

Codebook Dataset (ArcGIS FeatureServer)

The codebook dataset was retrieved using the custom get_arcgis_data() function, which connects to the UN SDG ArcGIS FeatureServer. This function queries the API in paginated batches and compiles the results into a DataFrame.

By specifying dataset="codebook", the function pulls metadata for all SDG indicators, including their codes, descriptions, thresholds, and reference years. This dataset acts as a structured reference for linking indicator codes with their detailed definitions throughout the analysis.

df_codebook = sdg.get_arcgis_data(dataset="codebook")
df_codebook.to_csv("df_codebook.csv", index=False)
#print(df_codebook.info())
df_codebook.head()

	IndCode	SDG	global	oecd	spillover	trend	trendoecd	Indicator	New	Period_Trends	...	greenthr	redthr	lower_bound	bestjust	dwldlink	Source	Description	Imputation	Computation	ObjectId
0	sdg1_wpc	1	yes	yes	None	yes	yes	Poverty headcount ratio at $2.15/day (%)	None	2015 - 2025	...	2	13	73	SDG Target	http://worldpoverty.io/	World Data Lab	Estimated percentage of the population that is...	Data was not reported for those countries wher...	None	1
1	sdg1_lmicpov	1	yes	yes	None	yes	yes	Poverty headcount ratio at $3.65/day (%)	None	2015 - 2025	...	2	13	52	SDG Target	http://worldpoverty.io/	World Data Lab	Estimated percentage of the population that is...	Data was not reported for those countries wher...	None	2
2	sdg1_oecdpov	1	no	yes	None	None	yes	Poverty rate after taxes and transfers (%)	None	2015 - 2022	...	10	15	18	Average of best performers	https://data-explorer.oecd.org/vis?tm=income%2...	OECD	The share of the population whose incomes fall...	None	None	3
3	sdg2_undernsh	2	yes	yes	None	yes	yes	Prevalence of undernourishment (%)	None	2015 - 2022	...	8	15	42	SDG Target	https://www.fao.org/faostat/en/#data/SDGB	FAO	The percentage of the population whose food in...	Due to uncertainty in the estimates, the lowes...	None	4
4	sdg2_stunting	2	yes	yes	None	yes	yes	Prevalence of stunting in children under 5 yea...	None	2015 - 2021	...	8	15	40	SDG Target	http://data.worldbank.org/indicator/SH.STA.STN...	UNICEF et al.	The percentage of children up to the age of 5 ...	UNICEF et al. (2016) report an average prevale...	None	5

5 rows × 22 columns

SDR 2025 Dataset (ArcGIS FeatureServer)

The SDR 2025 Dataset was collected using the custom get_arcgis_data() function with the parameter dataset="sdr2025". This call connects to the UN SDG ArcGIS FeatureServer and retrieves country-level SDG scores, rankings, spillover values, and progress measures.

The resulting dataset provides a structured view of each country’s performance across all 17 SDGs, including detailed goal-level scores and regional classifications. This dataset serves as the core reference for cross-country comparisons and performance evaluations.

df_sdr2025 = sdg.get_arcgis_data(dataset="sdr2025")
df_sdr2025.to_csv("df_sdr2025.csv", index=False)
print(df_sdr2025.info())
df_sdr2025.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 193 entries, 0 to 192
Columns: 685 entries, iso3 to ObjectId
dtypes: float64(393), int64(16), object(276)
memory usage: 1.0+ MB
None

	iso3	Name	Overall_Score	Overall_Rank	Spillover_Score	Spillover_Rank	IC	progress	Region	Goal_1_Rating	...	Goal_11_Score_reg	Goal_12_Score_reg	Goal_13_Score_reg	Goal_14_Score_reg	Goal_15_Score_reg	Goal_16_Score_reg	Goal_17_Score_reg	x	y	ObjectId
0	MNG	Mongolia	66.749047	100.0	92.467846	69.0	UMIC	7.662	East & South Asia	green	...	65.537978	77.954664	83.729895	61.512600	55.004292	60.092537	63.645575	105.574536	46.204958	1
1	ESP	Spain	81.037864	14.0	69.562800	136.0	HIC	2.730	OECD	yellow	...	94.042408	58.451979	73.317079	63.397362	70.569191	79.913873	69.569103	-3.557007	40.391352	2
2	CYP	Cyprus	73.755860	56.0	46.334067	163.0	HIC	4.327	E. Europe & C. Asia	green	...	84.669783	75.640405	84.610674	63.253842	73.796724	64.877297	69.436342	33.221763	35.045883	3
3	COD	Congo, Dem. Rep.	48.235735	162.0	94.902929	39.0	LIC	5.121	Sub-Saharan Africa	red	...	57.306788	92.914692	96.963757	67.021076	66.346338	49.194879	62.805388	23.400058	-1.095130	4
4	GNB	Guinea-Bissau	53.073429	153.0	89.055500	94.0	LIC	NaN	Sub-Saharan Africa	red	...	57.306788	92.914692	96.963757	67.021076	66.346338	49.194879	62.805388	-14.518245	12.244815	5

5 rows × 685 columns

SDR Backdated Dataset (ArcGIS FeatureServer)

The SDR Backdated Dataset was obtained using the custom get_arcgis_data() function with the parameter dataset="sdr_backdated". This call retrieves yearly SDG performance data from the UN SDG ArcGIS FeatureServer, covering multiple indicators across all goals.

The dataset provides a longitudinal perspective, including population, income classification, and goal-level scores by year for each country. This structure supports trend analysis and time-based evaluations of SDG progress.

df_backdated = sdg.get_arcgis_data(dataset="sdr_backdated")
df_backdated.to_csv("df_backdated.csv", index=False)
print(df_backdated.info())
df_backdated.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5225 entries, 0 to 5224
Columns: 331 entries, id to ObjectId
dtypes: float64(140), int64(25), object(166)
memory usage: 13.2+ MB
None

	id	Country	year	population	indexreg_	IC	sdgi_s	n_sdg1_wpc	n_sdg1_lmicpov	n_sdg2_undernsh	...	goal9	goal10	goal11	goal12	goal13	goal14	goal15	goal16	goal17	ObjectId
0	BEL	Belgium	2000	10242888.0	OECD	HIC	74	99.0	98.0	100.0	...	73	92	96	41.0	60.0	47	62.0	82.0	62	1
1	BEL	Belgium	2001	10285797.0	OECD	HIC	74	99.0	98.0	100.0	...	73	92	96	41.0	61.0	46	62.0	82.0	62	2
2	BEL	Belgium	2002	10332714.0	OECD	HIC	74	99.0	98.0	100.0	...	75	92	96	42.0	61.0	47	63.0	81.0	63	3
3	BEL	Belgium	2003	10378423.0	OECD	HIC	75	99.0	98.0	100.0	...	75	99	95	41.0	59.0	45	68.0	81.0	63	4
4	BEL	Belgium	2004	10426678.0	OECD	HIC	75	96.0	94.0	100.0	...	76	96	96	40.0	58.0	46	68.0	81.0	63	5

5 rows × 331 columns

---

Data Wrangling

The raw SDG data comes in many shapes and layers, with codes, names, and values scattered across different sources. Before any real analysis can begin, the data must be cleaned, reshaped, and aligned. This stage takes the scattered inputs and turns them into a consistent structure that can be used for ranking, correlation, and predictive work.

In this part of the project, I focus on bringing together the lookup file, the main SDG scores, and the metadata from the ArcGIS server. The steps include fixing column names, merging lookup keys with the indicator scores, grouping indicators under the broader dimensions (Environmental, Social, Economic, Partnership), and handling missing or misaligned entries.

The goal of this section is simple: make sure that all datasets speak the same language, so the later stages of analysis run smoothly. After wrangling, the project has a clean and consistent dataset that is ready for ranking comparisons, regional studies, and cross-goal corr

Building the Lookup Dataset

One of the main wrangling steps is creating a consistent lookup table that links SDG codes, goal numbers, groups, and descriptions. This helps align the multiple data sources and makes later analysis easier.

The process started by cleaning the df_goals dataset and extracting the goal codes, titles, and their assigned groups (e.g., Social, Economic, Environmental, Partnership). A new column was also added with the format SDG_x for clarity.

Next, the df_codebook was filtered to keep only the key fields: indicator code, SDG number, and indicator description. Both the goals and the codebook were merged into a single lookup table, ensuring that each SDG code maps to the correct group using a dictionary created from the goals dataset.

The final product is df_lookup, a tidy reference table with four columns:

• code → indicator or goal code
• sdg → SDG number
• group → assigned dimension (social, economic, environmental, partnership)
• description → full text of the goal or indicator

This table was also saved as df_lookup.csv for reuse. A preview of the output confirms that each SDG goal now has a clear mapping with its description and group assignment, making it an essential key for analysis.

df_goals["sdg"] = df_goals["code"].astype(int)
goals_lookup = pd.DataFrame({
    "code": df_goals["sdg"].apply(lambda x: f"SDG_{x}"),
    "sdg": df_goals["sdg"],
    "description": df_goals["title"],
    "group": df_goals["group"]
})

goal_group_map = df_goals.set_index("sdg")["group"]

df_codebook_clean = df_codebook[["IndCode", "SDG", "Indicator"]].copy()
codebook_lookup = pd.DataFrame({
    "code": df_codebook_clean["IndCode"],
    "sdg": df_codebook_clean["SDG"].astype(int),
    "description": df_codebook_clean["Indicator"]
})

codebook_lookup["group"] = codebook_lookup["sdg"].map(goal_group_map)
df_lookup = pd.concat([goals_lookup, codebook_lookup], ignore_index=True)
df_lookup = df_lookup[["code", "sdg", "group", "description"]]

df_lookup["code"] = df_lookup["code"].replace(r'^sdg', 'IND_', regex=True)
df_lookup["code"] = df_lookup["code"].replace(r'^Goal_', 'SDG_', regex=True)


df_lookup.to_csv("df_lookup.csv", index=False)
df_lookup.head()

	code	sdg	group	description
0	SDG_1	1	social	No Poverty
1	SDG_2	2	social	Zero Hunger
2	SDG_3	3	social	Good Health and Well-being
3	SDG_4	4	social	Quality Education
4	SDG_5	5	social	Gender Equality

df_lookup2 = sdg.classify_groups(df_lookup, class_code=2) # Barbier & Burgess

Cleaning and Restructuring the Backdated Dataset

The backdated SDG dataset contains historical values that need to be reshaped before analysis. The raw file includes redundant fields and inconsistent naming across indicators and goals. To make it usable, several cleaning operations were applied.

First, duplicate or unnecessary columns such as indexreg_1 were dropped. Column names were then standardized:

• Goal columns were renamed into the format SDG_x.
• Indicator codes were simplified by removing prefixes such as n_.

Next, the dataset was restricted to keep only the essential fields: country, year, and all columns starting with sdg or SDG_. This ensures a clear separation of metadata (country, region, year) and performance scores (goals and indicators). An extra column sdgi_s, if present, was removed since it was not relevant to the analysis.

The cleaned dataset now has 4,825 rows and 123 columns, covering yearly SDG values for each country. A preview confirms that the data is structured by country and year, with aligned goal and indicator scores that are ready for further aggregation and comparison.

df_backdated_st = df_backdated.copy()
df_backdated_st = df_backdated_st.dropna(subset=["indexreg_"])
df_backdated_st = df_backdated_st.rename(columns=lambda x: f"SDG_{x[4:]}" if x.lower().startswith("goal") else x)
df_backdated_st = df_backdated_st.rename(columns=lambda x: x.replace("n_", "") if x.startswith("n_") else x)

df_backdated_st = df_backdated_st[
    ['id', 'Country', 'year'] +
    [col for col in df_backdated_st.columns if col.startswith('indexreg_') or col.startswith('sdg') or col.startswith('SDG_')]
]

if "sdgi_s" in df_backdated_st.columns:
    df_backdated_st = df_backdated_st.drop(columns=["sdgi_s"])

df_backdated_st = df_backdated_st.rename(columns=lambda x: x.replace("sdg", "IND_") if x.startswith("sdg") else x)
df_backdated_st = df_backdated_st.rename(columns=lambda x: x.replace("Goal_", "SDG_") if x.startswith("Goal_") else x)

df_backdated_st.to_csv("df_backdated_st.csv", index=False)
print(df_backdated_st.shape)
df_backdated_st.head()

(4825, 123)

	id	Country	year	indexreg_	IND_1_wpc	IND_1_lmicpov	IND_2_undernsh	IND_2_stunting	IND_2_wasting	IND_2_mdd	...	SDG_8	SDG_9	SDG_10	SDG_11	SDG_12	SDG_13	SDG_14	SDG_15	SDG_16	SDG_17
0	BEL	Belgium	2000	OECD	99.0	98.0	100.0	96.0	98.0	100.0	...	80.0	73	92	96	41.0	60.0	47	62.0	82.0	62
1	BEL	Belgium	2001	OECD	99.0	98.0	100.0	96.0	98.0	100.0	...	81.0	73	92	96	41.0	61.0	46	62.0	82.0	62
2	BEL	Belgium	2002	OECD	99.0	98.0	100.0	96.0	98.0	100.0	...	80.0	75	92	96	42.0	61.0	47	63.0	81.0	63
3	BEL	Belgium	2003	OECD	99.0	98.0	100.0	96.0	98.0	100.0	...	79.0	75	99	95	41.0	59.0	45	68.0	81.0	63
4	BEL	Belgium	2004	OECD	96.0	94.0	100.0	96.0	98.0	100.0	...	79.0	76	96	96	40.0	58.0	46	68.0	81.0	63

5 rows × 123 columns

Preparing the SDR 2025 Dataset

The SDR 2025 dataset provides the most recent snapshot of SDG performance across countries and regions. However, the raw structure includes verbose column names and inconsistent formatting. To make this dataset consistent with the rest of the project, several cleaning steps were applied.

First, only the key identifying fields (iso3, Name, Region) and the goal and indicator columns were kept. Columns that ended with _Score were renamed by dropping the suffix, leaving a cleaner format such as SDG_1, SDG_2, and so forth. Similarly, all sdg indicators were preserved in their shortened form for easy reference.

After this restructuring, the dataset now contains 193 rows and 122 columns. Each row corresponds to a country with its associated region and ISO code, followed by performance scores across all 17 goals and the supporting SDG indicators. A quick preview confirms that the data is compact, with values ready to be compared, ranked, and correlated with the historical backdated dataset.

df_sdr2025_st = df_sdr2025.copy()
df_sdr2025_st = df_sdr2025_st.rename(columns=lambda x: x.replace("Score_", "") if x.startswith("Score_") else x)
df_sdr2025_st = df_sdr2025_st[
    ['iso3', 'Name', 'Region'] +
    [
        col for col in df_sdr2025_st.columns
        if col.startswith('Goal_') and col.endswith('_Score') or col.startswith('sdg') or col.endswith('Score_reg')
    ]
]
df_sdr2025_st = df_sdr2025_st.rename(
    columns=lambda c: re.sub(r'^(?!Score_reg_)(.+)_Score_reg$', r'Score_reg_\1', c)
)
df_sdr2025_st = df_sdr2025_st.rename(columns=lambda x: x.replace("_Score", "") if x.startswith('Goal_') and x.endswith("_Score") else x)

df_sdr2025_st = df_sdr2025_st.rename(columns=lambda x: x.replace("sdg", "IND_") if x.startswith('sdg') else x)
df_sdr2025_st = df_sdr2025_st.rename(columns=lambda x: x.replace("Goal_", "SDG_") if x.startswith('Goal_') else x)

print(df_sdr2025_st.shape)
df_sdr2025_st.to_csv("df_sdr2025_st.csv", index=False)
df_sdr2025_st.head()

(193, 139)

	iso3	Name	Region	SDG_1	SDG_2	SDG_3	SDG_4	SDG_5	SDG_6	SDG_7	...	Score_reg_Goal_8	Score_reg_Goal_9	Score_reg_Goal_10	Score_reg_Goal_11	Score_reg_Goal_12	Score_reg_Goal_13	Score_reg_Goal_14	Score_reg_Goal_15	Score_reg_Goal_16	Score_reg_Goal_17
0	MNG	Mongolia	East & South Asia	98.2085	46.053750	68.423000	90.119000	69.739500	71.4756	38.31625	...	72.805989	52.317290	74.852594	65.537978	77.954664	83.729895	61.512600	55.004292	60.092537	63.645575
1	ESP	Spain	OECD	99.3040	69.847111	94.903286	97.244750	87.503500	85.2658	79.15825	...	78.405904	84.820934	79.378092	94.042408	58.451979	73.317079	63.397362	70.569191	79.913873	69.569103
2	CYP	Cyprus	E. Europe & C. Asia	99.9090	63.460111	90.787692	98.935500	69.141333	69.1992	75.36700	...	71.496805	51.332643	86.184429	84.669783	75.640405	84.610674	63.253842	73.796724	64.877297	69.436342
3	COD	Congo, Dem. Rep.	Sub-Saharan Africa	1.8410	43.622556	39.717000	42.129667	40.691500	41.2382	30.07600	...	66.788268	26.614957	48.683300	57.306788	92.914692	96.963757	67.021076	66.346338	49.194879	62.805388
4	GNB	Guinea-Bissau	Sub-Saharan Africa	34.1335	38.782875	36.318231	25.083250	38.524750	39.0965	20.66350	...	66.788268	26.614957	48.683300	57.306788	92.914692	96.963757	67.021076	66.346338	49.194879	62.805388

5 rows × 139 columns

Combining Historical and SDR 2025 Data

Once both the backdated and SDR 2025 datasets were cleaned, they were merged into a single unified dataset for analysis. To do this, column names were standardized across both sources so that the merge would align correctly (e.g., matching id vs iso3, and Name vs Country).

The SDR 2025 dataset was assigned a fixed year value of 2025 and then concatenated with the historical backdated dataset. After merging, the combined frame was sorted by country and year to ensure a clear chronological order.

To prepare the data for quantitative analysis, all columns containing SDG goals or indicators were converted to numeric values where possible. Any non-numeric entries were coerced to nulls to prevent errors. Finally, values were rounded for consistency and readability.

The result is a comprehensive dataset, df_sdg, which spans multiple years (2000 through 2025) and aligns each country’s SDG performance over time. This dataset will serve as the foundation for ranking analysis, regional comparisons, and correlation studies in the following sections.

df_backdated_st = df_backdated_st.rename(columns={"id": "ID", "year": "Year", "indexreg_": "Region"})
df_sdr2025_st = df_sdr2025_st.rename(columns={"iso3": "ID", "Name": "Country"})
df_sdr2025_st.insert(2, "Year", 2025)
df_sdg = pd.concat([df_backdated_st, df_sdr2025_st], ignore_index=True, join="outer", sort=False)
df_sdg = df_sdg.sort_values(by=["Country", "Year"]).reset_index(drop=True)

num_cols = [col for col in df_sdg.columns if "IND_" in col or "SDG_" in col]
df_sdg[num_cols] = df_sdg[num_cols].apply(pd.to_numeric, errors="coerce")
#df_sdg = df_sdg.round(0)

Standardizing Region Names

The raw SDG datasets contain region labels that are inconsistent or too detailed for clean analysis. For example, some regions appear with long names such as "E. Europe & C. Asia" while others use mixed formats like "Western Europe (non-OECD)" or "Sub-Saharan Africa". To ensure consistency across all countries and years, these labels were simplified and standardized.

A mapping dictionary was created to replace the verbose region names with shorter, unified codes. Examples include:

• "E. Europe & C. Asia" → "E_Euro_Asia"
• "Western Europe (non-OECD)" → "W_Europe"
• "Sub-Saharan Africa" → "Africa"
• "East & South Asia" → "E_S_Asia"

After applying the mapping, the Region column now has consistent labels that make it easier to group, filter, and visualize data. This small but important step prevents errors in aggregation and ensures that regional analyses later in the project are clear and reliable.

print(df_sdg["Region"].unique())

['E_Euro_Asia' 'E. Europe & C. Asia' 'MENA' 'W_Europe'
 'Western Europe (non-OECD)' 'Africa' 'Sub-Saharan Africa' 'LAC' 'OECD'
 'E_S_Asia' 'East & South Asia' 'Oceania']

region_map = {
    "E. Europe & C. Asia": "E_Euro_Asia",
    "Western Europe (non-OECD)": "W_Europe",
    "Sub-Saharan Africa": "Africa",
    "East & South Asia": "E_S_Asia"
}
df_sdg["Region"] = df_sdg["Region"].replace(region_map)

df_sdg.head()

	ID	Country	Year	Region	IND_1_wpc	IND_1_lmicpov	IND_2_undernsh	IND_2_stunting	IND_2_wasting	IND_2_mdd	...	Score_reg_Goal_8	Score_reg_Goal_9	Score_reg_Goal_10	Score_reg_Goal_11	Score_reg_Goal_12	Score_reg_Goal_13	Score_reg_Goal_14	Score_reg_Goal_15	Score_reg_Goal_16	Score_reg_Goal_17
0	AFG	Afghanistan	2000	E_Euro_Asia	40.0	0.0	0.0	0.0	44.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	AFG	Afghanistan	2001	E_Euro_Asia	32.0	0.0	0.0	0.0	44.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	AFG	Afghanistan	2002	E_Euro_Asia	45.0	0.0	0.0	0.0	44.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	AFG	Afghanistan	2003	E_Euro_Asia	48.0	0.0	9.0	0.0	44.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	AFG	Afghanistan	2004	E_Euro_Asia	46.0	0.0	16.0	0.0	44.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 140 columns

df_sdg.to_csv("df_sdg.csv", index=False)

## Force ploty on Kaggle

from IPython.display import HTML, Image, display
import plotly.io as pio

def show_fig(fig, height_px=520):
    """Always renders: embeds plotly.js + the figure HTML right in the cell."""
    html = fig.to_html(include_plotlyjs=True, full_html=False)
    display(HTML(f'<div style="height:{height_px}px">{html}</div>'))

---

Global SDG Analysis

With the datasets cleaned and combined into a unified structure, the next stage of the project is to explore how countries and regions perform on the Sustainable Development Goals. This section focuses on identifying global patterns, regional differences, and changes over time.

The analysis begins with rankings of goals, groups, and regions to highlight leaders and laggards. It then expands into timelines that track SDG progress year by year, allowing us to see whether countries are moving forward or falling behind. Special attention is given to specific goals such as Goal 9 (industry, innovation, and infrastructure) and Goal 13 (climate action), which capture both economic and environmental priorities.

The purpose of this section is to move from raw data to meaningful insights. By comparing scores across goals and regions, and by following their trends through time, the analysis provides a clear view of how the SDGs interact on a global scale. These findings also prepare the ground for the deeper correlation studies that follow.

---

Ranking of SDGs in 2025

This chart highlights how the seventeen Sustainable Development Goals compare against one another based on their mean scores for the year 2025.

Environmental goals appear dominant, with Goal 13 (Climate Action) clearly leading, followed by Goal 12 (Responsible Consumption and Production). Social targets like Goal 1 (No Poverty) and Goal 4 (Quality Education) also perform strongly, securing high positions.

Economic-oriented goals show more variation: while Goal 8 (Decent Work and Economic Growth) remains competitive, Goal 9 (Industry, Innovation and Infrastructure) ranks at the bottom, signaling uneven progress across regions.

The distribution makes it obvious that momentum toward environmental and core social aims is holding steady, but infrastructure and industrial transformation are lagging. This imbalance may suggest where global and regional priorities are misaligned, pointing to areas needing sharper policy focus.

In summary, 2025 continues to show a push on environmental resilience and poverty reduction, yet structural economic innovation still struggles to keep pace.

fig = sdg.plot_goal_ranking(df_sdg, df_lookup, 2025, rank_by="goal")
fig.show()
#print(sdg.generate_nlp_insight(fig))

---

Timelines of the Top 5 SDGs for 2025

This chart presents the performance over time of the five highest scoring SDGs in 2025: Goal 13 (Climate Action), Goal 12 (Responsible Consumption and Production), Goal 1 (No Poverty), Goal 4 (Quality Education), and Goal 11 (Sustainable Cities and Communities). They are highlighted because they collectively define the upper edge of global progress in the current assessment year.

Goal 13 leads consistently, holding the strongest trajectory across the full period. Goal 12 follows with steady stability in the mid 70s, while Goal 1 and Goal 4 both show sharp upward climbs, reflecting significant long-term improvement in poverty reduction and education outcomes. Goal 11, though flatter in shape, maintains solid strength, remaining in the 70s and reinforcing the importance of sustainable cities as part of the global agenda.

Together, these five goals illustrate how the highest-performing SDGs combine both ecological and social dimensions. They reveal a blend of long-term stability, sharp gains in human development, and the persistent leadership of climate action at the top.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, items=["SDG_13","SDG_12","SDG_1","SDG_4","SDG_11"])
fig.show()

---

Timelines of the Bottom 5 SDGs for 2025

This chart highlights the five lowest scoring SDGs in 2025: Goal 2 (Zero Hunger), Goal 5 (Gender Equality), Goal 7 (Affordable and Clean Energy), Goal 9 (Industry, Innovation and Infrastructure), and Goal 16 (Peace, Justice and Strong Institutions). They are shown here as a collective lens on where global progress remains weakest.

Goal 16, though higher than its peers for much of the timeline, drifts downward slightly in recent years and settles among the bottom group. Goals 2, 5, and 7 cluster closely, displaying modest long-term gains but never managing to break beyond the low 60s. Goal 9, while starting far below the others, demonstrates the steepest climb, moving from below 30 in 2000 to just above 50 by 2025, yet still remains the lowest ranked within the set.

Taken together, these goals reveal structural areas where development continues to falter. Hunger, gender equality, and energy access remain stubbornly difficult to solve, while peace and justice institutions face setbacks.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, items=["SDG_2","SDG_5","SDG_7","SDG_9","SDG_16"])
fig.show()

Percent change of SDG goals from 2000 to 2025

This chart shows how each goal moved from the start year to the end year. Bars to the right mean the goal improved. Bars to the left would mean decline. The label at the end of each bar gives the percent change.

Percent change of Goals

1. Goal 9 has the strongest rise at about eighty eight percent. This tells a clear story of long run gains in industry, innovation, and infrastructure.
2. Goal 5 and Goal 1 follow next at about thirty five percent and twenty seven percent. This supports steady progress in gender outcomes and poverty reduction.
3. Goal 3 rises by about twenty three percent, then Goal 7 and Goal 10 are in the mid teens. Health, clean energy, and inequality all show healthy upward movement.
4. A cluster of goals sits in the single digits. These include Goal 6, Goal 17, Goal 8, Goal 2, Goal 11, and Goal 12. They are improving but slowly.
5. Goal 13 is near flat. Goal 16 is slightly below zero, a small decline relative to its year 2000 level.
6. Pattern by dimensions social and economic goals generally move up together. Environmental related goals show smaller changes, which hints at harder progress on that pillar.

Industry and innovation leads by a long way. Gender, poverty, and health also gain strongly. Many other goals move a little. Peace and institutions shows a small backward step.

df_pct, fig = sdg.pct_change_sdg(df_sdg, df_lookup, start_year=2000, end_year=2025, level="goal", sort_desc=False)
fig.show()

df_pct

	code	start	end	delta	pct_change	group	description	label
0	SDG_16	65.01	61.79	-3.22	-4.95	social	Peace, Justice & Strong Institutions	SDG_16
1	SDG_13	85.08	83.88	-1.21	-1.42	environmental	Climate Action	SDG_13
2	SDG_12	76.60	77.22	0.62	0.81	economic	Responsible Consumption & Production	SDG_12
3	SDG_11	71.45	73.80	2.35	3.28	social	Sustainable Cities & Communities	SDG_11
4	SDG_8	68.29	71.13	2.85	4.17	economic	Decent Work & Economic Growth	SDG_8
5	SDG_2	55.77	58.12	2.35	4.22	social	Zero Hunger	SDG_2
6	SDG_17	62.98	66.97	3.98	6.32	partnership	Partnerships for the Goals	SDG_17
7	SDG_6	63.74	69.29	5.55	8.70	environmental	Clean Water and Sanitation	SDG_6
8	SDG_14	59.36	64.65	5.29	8.92	environmental	Life Below Water	SDG_14
9	SDG_4	65.51	74.69	9.17	14.00	social	Quality Education	SDG_4
10	SDG_15	56.66	65.13	8.47	14.94	environmental	Life on Land	SDG_15
11	SDG_7	52.49	62.73	10.24	19.51	social	Affordable and Clean Energy	SDG_7
12	SDG_10	55.33	66.15	10.82	19.55	economic	Reduced Inequalities	SDG_10
13	SDG_3	58.15	71.61	13.47	23.16	social	Good Health and Well-being	SDG_3
14	SDG_1	59.26	75.30	16.04	27.08	social	No Poverty	SDG_1
15	SDG_5	46.32	62.33	16.01	34.56	social	Gender Equality	SDG_5
16	SDG_9	27.41	51.53	24.11	87.96	economic	Industry, Innovation & Infrastructure	SDG_9

---

Percent change of SDG indicators

1. Digital access surges. Internet use, mobile broadband, and related Goal 9 tech indicators post very large positive gains.
2. Human capital indicators also rise. Seats held by women in parliament, articles published, and universal health coverage grow strongly.
3. Several indicators decline. Press freedom drops, adult obesity rises so its score falls, labor rights protection weakens, and fish stocks plus red list status also fall.
4. The spread across indicators is very wide. Some values are triple digit positive while a few are sharply negative. This happens when the baseline in two thousand is very small or when a measure deteriorates steadily.
5. Economic and innovation signals dominate the high growth side. Many environmental and governance signals sit near zero change or negative.
6. Policy read. Big gains cluster in a few Goal 9 indicators. Areas that need attention include governance and safety, oceans and biodiversity, and unhealthy weight under Goal 2.

Tech access and information capacity rise fast, human capital improves, but governance, oceans, biodiversity, and obesity related measures show decline or slow movement.

df_pct, fig = sdg.pct_change_sdg(df_sdg, df_lookup, start_year=2000, end_year=2025, level="sdg", sort_desc=False)
fig.show()

df_pct

	code	start	end	delta	pct_change	group	description	sdg	label
0	IND_16_rsf	58.99	36.17	-22.82	-38.69	social	Press Freedom Index (worst 0-100 best)	16	IND_16_rsf (Goal 16)
1	IND_2_obesity	66.84	44.29	-22.55	-33.73	social	Prevalence of obesity, BMI ≥ 30 (% of adult po...	2	IND_2_obesity (Goal 2)
2	IND_8_rights	58.46	51.37	-7.09	-12.12	economic	Fundamental labor rights are effectively guara...	8	IND_8_rights (Goal 8)
3	IND_14_fishstocks	78.92	71.77	-7.15	-9.06	environmental	Fish caught from overexploited or collapsed st...	14	IND_14_fishstocks (Goal 14)
4	IND_15_redlist	69.84	65.08	-4.76	-6.81	environmental	Red List Index of species survival (worst 0-1 ...	15	IND_15_redlist (Goal 15)
...	...	...	...	...	...	...	...	...	...
97	IND_3_uhc	20.20	45.45	25.26	125.05	social	Universal health coverage (UHC) index of servi...	3	IND_3_uhc (Goal 3)
98	IND_9_articles	17.21	41.00	23.79	138.21	economic	Articles published in academic journals (per 1...	9	IND_9_articles (Goal 9)
99	IND_5_parl	21.08	51.55	30.47	144.52	social	Seats held by women in national parliament (%)	5	IND_5_parl (Goal 5)
100	IND_9_intuse	6.79	68.56	61.77	909.30	economic	Population using the internet (%)	9	IND_9_intuse (Goal 9)
101	IND_9_mobuse	7.23	76.22	69.00	954.56	economic	Mobile broadband subscriptions (per 100 popula...	9	IND_9_mobuse (Goal 9)

102 rows × 9 columns

---

Goal 13 - The Highest SDG Score for 2025

---

Regional Ranking for Goal 13

Goal 13 (Climate Action) stands out as the highest scoring goal in 2025.
This chart shows how the regions stack up in driving that success.

Africa and Oceania lead the way, almost tied at the very top.
Latin America and the Caribbean come next, followed closely by Eastern Europe & Central Asia and East & South Asia.
OECD and MENA land lower on the ladder, with Western Europe rounding out the group.

The picture highlights not just the global strength of Goal 13, but also the uneven spread of regional contributions — some regions pushing the score skyward, others lagging further back.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_13', group_col='Region', top_n=8, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Regional Trends for Goal 13

This plot tracks Goal 13, Climate Action, across all major regions from 2000 to 2025. Goal 13 is highlighted here because it ranks as the highest scoring SDG in 2025.

The trajectories show variation between regions. Africa and Oceania climb strongly over time, closing gaps with higher-scoring regions. OECD and Western Europe hold steady at the upper end, while Latin America, Eastern Europe and Central Asia, and East and South Asia sustain moderate but consistent scores. MENA remains the lowest performer, though with signs of recovery after earlier decline.

The picture confirms why Goal 13 has become the benchmark for this comparison: it not only leads globally, but it also reveals how different regions contribute unevenly to the broader push against climate change.

fig = sdg.plot_goal_entity_timeline(
    df_sdg, df_lookup,
    goal="SDG_13",
    entity_type="Region",
    top_n=8, top_mode="top"
)
fig.show()

---

Top 5 Countries Driving Goal 13

Goal 13 (Climate Action) is the leading global goal in 2025, and the strongest contributions come out of Africa.

The chart shows the top five countries, all from the African region, that stand at the very front of this achievement.
Central African Republic, Burundi, and Guinea-Bissau each hit the perfect 100 mark.
Burkina Faso and Chad follow close behind, only a step down at 99.

This sharp dominance highlights how Africa is not only a central player but the defining region in pushing Goal 13 to the very top.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_13', group_col='Country', top_n=5, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Timeline of Top 5 Countries for Goal 13

This chart shows the performance of the five best-scoring countries for Goal 13 (Climate Action) in 2025: Central African Republic, Burundi, Guinea-Bissau, Burkina Faso, and Chad. Goal 13 is the highest ranked SDG globally, and these countries stand at the front line of that achievement.

The trajectories here remain remarkably stable at the highest end of the scale. Guinea-Bissau consistently records perfect scores of 100 across the entire timeline, while the others sustain near-perfect levels, only showing minor fluctuations. Burkina Faso, Chad, and their peers hover close to the ceiling, with very little downward movement.

This timeline underlines how some nations have managed to align strongly with climate action goals, achieving sustained excellence over decades. In contrast to the weaker performers, these countries exemplify what consistent commitment to environmental action can deliver when measured against the SDG benchmarks.

fig = sdg.plot_goal_entity_timeline(
    df_sdg, df_lookup,
    goal="SDG_13",
    entity_type="Country",
    top_n=5, top_mode="top"
)
fig

---

Bottom 5 Countries for Goal 13

Although Goal 13 (Climate Action) leads globally in 2025, not all regions are equally strong.

The chart highlights the five lowest-scoring countries. Four of them are in the MENA region: Qatar, United Arab Emirates, Kuwait, and Bahrain. Brunei Darussalam (East & South Asia) also appears in this group.

Qatar ranks the lowest at only 3 points, while Bahrain holds the highest position among the bottom group at 19 points.

This contrast against Africa’s top performance shows how unevenly the progress for Goal 13 is spread across the world.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_13', group_col='Country', top_n=5, ascending=True, year=2025, plot_theme="plotly_dark")
fig.show()

---

Timeline of Bottom 5 Countries for Goal 13

Goal 13, Climate Action, is the highest scoring SDG globally in 2025. However, when viewed at the country level, major disparities appear. This timeline shows the bottom five countries for Goal 13: Qatar, United Arab Emirates, Brunei Darussalam, Kuwait, and Bahrain.

Across the years, these nations remain locked at the lower end of the scale, with scores showing irregular movement and little sustained upward momentum. A few short-lived gains surface in the record, but they fail to establish long-term improvement.

The timeline makes clear that global leadership in Goal 13 masks uneven progress. Even as climate action drives the highest global scores, certain countries continue to lag far behind, illustrating the deep divides in how environmental goals are being realized.

fig = sdg.plot_goal_entity_timeline(
    df_sdg, df_lookup,
    goal="SDG_13",
    entity_type="Country",
    top_n=5, top_mode="bottom"
)
fig.show()

---

Ranking of Goal 13 Indicators

This chart captures the performance of Goal 13 indicators in 2025, showing how different measures of climate action stack against each other.

• sdg13_co2export stands well above the rest with a score of 92.01, reflecting strong progress in reducing CO₂ emissions tied to fossil fuel exports.
• sdg13_ghgimport follows with 79.93, pointing to moderate outcomes in managing greenhouse gas emissions linked to imports.
• sdg13_co2gcp sits close at 79.14, showing progress in reducing per-capita CO₂ emissions but still lagging behind the export-focused indicator.

The results suggest that while export-related emissions (sdg13_co2export) show the most tangible improvement, import-linked and per-capita measures remain areas where greater efforts are needed to balance climate commitments across all dimensions of Goal 13.

fig = sdg.plot_goal_ranking(df_sdg, df_lookup, 2025, rank_by="sdg", goal_filter="SDG_13", fig_height=300)
fig.show()

---

Regional ranking for Goal 13 indicator: CO₂ from exports (2025)

This plot lines up the regions on how they fared with sdg13_co2export in 2025. The story is uneven, with some regions nearly maxing out while others still lag far behind.

• Africa takes the crown, hitting 98.51, showing export flows tied to carbon are almost negligible.
• Oceania and Eastern Europe & Central Asia hover close behind, both above 95, pointing to sharp progress in lowering the carbon weight of exports.
• Latin America & the Caribbean and the OECD bloc are not far off, steady in the low-90s, showing consistent but less dramatic gains.
• East & South Asia sits at 90.35, holding the line but not breaking into the top cluster.
• MENA, however, drops sharply to 63.23, standing out as the region where export emissions still remain heavy and unresolved.

The spread tells us something critical: while parts of the world have nearly cleaned up export-related emissions, others remain tied to high-carbon trade. Goal 13 here is not just about emissions overall, but how trade flows can tilt the climate balance.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='IND_13_co2export', group_col='Region', top_n=8, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Timeline of Goal 13 Indicators

Goal 13, which focuses on urgent climate action, is measured not only by its overall score but also through several detailed indicators.
This timeline traces three key measures tied to emissions and energy use:

• sdg13_co2gcp – CO₂ related to global consumption and production.
• sdg13_ghgimport – greenhouse gas emissions linked with imports.
• sdg13_co2export – CO₂ emissions tied to exports.

From the chart, one clear feature is the steady line of export emissions (sdg13_co2export), staying almost unchanged over two decades, only showing a slight decline at the end.
In contrast, domestic CO₂ from production (sdg13_co2gcp) and import-related greenhouse gases (sdg13_ghgimport) both show long cycles of rise and fall, with small fluctuations. Import emissions especially show spikes around 2009 and 2020, reflecting external shocks in trade and global activity.

This reveals that while export emissions remained stubbornly high, domestic and import-related emissions had more variability, suggesting differences in policy pressure and economic dynamics.
Overall, Goal 13 indicators show mixed progress: flatlining exports versus modest improvements elsewhere, hinting that climate policies have uneven impacts depending on whether emissions are produced, imported, or exported.

goal_ind = (df_lookup.loc[df_lookup["sdg"] == 13, "code"].tolist())
goal_ind = [c for c in goal_ind if not c.startswith("SDG_")]
goal_ind

['IND_13_co2gcp', 'IND_13_ghgimport', 'IND_13_co2export', 'IND_13_ecr']

goal_ind = (df_lookup.loc[df_lookup["sdg"] == 13, "code"].tolist())
goal_ind = [c for c in goal_ind if not c.startswith("SDG_")]
goal_ind

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, items=goal_ind)
fig.show()

---

Timeline of Goal 13 Indicators by Region

This figure traces three core Goal 13 (Climate Action) indicators across regions between 2000 and 2025. Each subplot captures a different dimension of climate-related pressures and responsibilities.

• sdg13_co2gcp (CO₂ emissions from fossil fuel combustion and cement production):
  Emissions per capita have been historically high in Oceania, OECD, and E_Euro_Asia, while Africa and MENA remain much lower. Western Europe shows gradual improvement, hinting at transition efforts, but industrial-heavy regions sustain high levels.

• sdg13_ghgimport (GHG emissions embodied in imports):
  Advanced economies, particularly Oceania and OECD, consistently display higher import-related footprints. This highlights how global trade displaces emissions, with developing regions such as Africa and E_S_Asia showing comparatively lighter values.

• sdg13_co2export (CO₂ embodied in fossil fuel exports):
  Export-linked emissions remain dominated by resource-rich regions like Africa, LAC, and E_Euro_Asia, where the export structure drives consistently high values. OECD, W_Europe, and E_S_Asia sit lower, reflecting diversified economies and reduced export reliance.

Key Takeaway:
While Goal 13 is the top-scoring SDG overall in 2025, these indicators underscore deep regional contrasts. Developed regions display strong consumption footprints (via imports), while developing and resource-exporting regions carry the burden of production- and export-based emissions. The timeline stresses that meaningful climate action requires tackling both consumption and production chains, not one side alone.

fig1 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_13_co2gcp", entity_type="Region", top_mode="top")
fig2 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_13_ghgimport", entity_type="Region", top_mode="top")
fig3 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_13_co2export", entity_type="Region", top_mode="top")
combined = sdg.combine_figs([fig1, fig2, fig3], rows=3, width=1000, height=1000, main_title="Timeline of Goal 13 Indicators by Region")
combined.show()

---

Top 5 Positive Correlations with Goal 13: Climate Action (2025)

This table shows the strongest correlations between Goal 13 and other goals in 2025.

• Goal 12 has the highest positive correlation (0.74).
• Goal 14 and Goal 15 also show smaller positive links.
• Goal 17 and Goal 8 appear but their values are weak and close to zero.

This means climate action is most connected with sustainable consumption and ecosystem goals.

df_corr = sdg.correlate_with_target(df_sdg, df_lookup, target="SDG_13", year=2025, top_n=5, sign="pos")
sdg.format_corr_table(df_corr)

### Correlations with SDG_13: Climate Action

	Code	Description	Correlation	Abs.Correlation
0	SDG_12	Responsible Consumption & Production	0.74	0.74
1	SDG_14	Life Below Water	0.13	0.13
2	SDG_15	Life on Land	0.10	0.10
3	SDG_17	Partnerships for the Goals	-0.09	0.09
4	SDG_8	Decent Work & Economic Growth	-0.17	0.17

---

Top 5 Negative Correlations with Goal 13: Climate Action (2025)

This table shows the strongest negative correlations between Goal 13 and other goals in 2025.

• Goal 9 has the largest negative link (-0.59).
• Goal 3 and Goal 16 also show strong negative relationships.
• Goal 1 and Goal 4 are weaker but still negative.

This means climate action tends to trade off against infrastructure, health, justice, poverty, and education in some cases.

df_corr = sdg.correlate_with_target(df_sdg, df_lookup, target="SDG_13", year=2025, top_n=5, sign="neg")
sdg.format_corr_table(df_corr)

### Correlations with SDG_13: Climate Action

	Code	Description	Correlation	Abs.Correlation
0	SDG_9	Industry, Innovation & Infrastructure	-0.59	0.59
1	SDG_3	Good Health and Well-being	-0.57	0.57
2	SDG_16	Peace, Justice & Strong Institutions	-0.54	0.54
3	SDG_1	No Poverty	-0.51	0.51
4	SDG_4	Quality Education	-0.42	0.42

---

Goal 9 - The Lowest SDG Score for 2025

---

Regional Ranking for Goal 9

Goal 9 (Industry, Innovation, and Infrastructure) stands out as the lowest scoring SDG of 2025. This regional ranking highlights the uneven landscape across different parts of the world.

At the top, the OECD dominates with a strong score above 84, reflecting long-standing investments in industrial development and technological capacity. The MENA region follows at a much lower level, around the upper fifties, with East & South Asia and Eastern Europe & Central Asia clustered slightly above the fifty-point line.

Latin America and Oceania trail further behind, landing in the forties, while Africa records the weakest outcome of all regions at just 26.6. Western Europe (non-OECD) also sits at the bottom tier, showing that even regions with advanced economies can register weak results under this specific measure.

The chart underscores that while some regions lead in innovation and infrastructure, large portions of the globe remain structurally disadvantaged, reinforcing why Goal 9 ranks lowest among all SDGs in 2025.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_9', group_col='Region', top_n=8, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Regional Trend for Goal 9

Goal 9, focused on industry, innovation, and infrastructure, remains the lowest performing SDG in 2025. However, the regional timeline shows a story of steady but uneven growth across the globe.

The OECD has consistently led, starting from the late 50s in 2000 and rising steadily into the mid-80s by 2025. Western Europe also demonstrates strong progress, climbing from the mid-40s to the mid-70s, though still trailing the OECD.

Regions such as MENA and East & South Asia reveal sharper upward momentum, especially after 2010, moving from the mid-20s into the 50s range by 2025. Eastern Europe & Central Asia and Latin America also follow a rising but more moderate path.

By contrast, Africa and Oceania remain at the lower end of the spectrum, starting from near stagnant levels in the early 2000s and only slowly advancing. Africa, in particular, shows the weakest improvement, barely approaching 27 by 2025.

This timeline illustrates that while Goal 9 is universally challenging, the gap between advanced and developing regions remains stark. The OECD’s climb highlights how infrastructure and innovation can accelerate long-term progress, while other regions continue to lag behind due to structural barriers.

fig = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="SDG_9", entity_type="Region")
fig.show()

---

Top 5 Countries Driving Goal 9

Goal 9 (industry, innovation, and infrastructure) may stand as the lowest scoring SDG globally, but the top-performing countries reveal how advanced economies are setting the pace.

The leading nations are overwhelmingly clustered within the OECD, underscoring the advantage of developed infrastructure and sustained investment in technology. Korea, Rep. achieves a perfect score of 100, reflecting its high industrial capacity and innovation systems. The United States follows closely at 99, with Austria, Israel, and Switzerland all posting 98.

This concentration of high scores within OECD members shows a sharp divide between advanced and developing economies. While much of the world struggles to lift Goal 9 performance, these countries demonstrate what is possible when resources, governance, and innovation ecosystems align. They serve as benchmarks in bridging the global industrial gap, though their success also highlights the persistent inequity in SDG progress.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_9', group_col='Country', top_n=5, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Timeline of the Top 5 Countries for Goal 9

Goal 9 — the push for resilient infrastructure, sustainable industry, and real innovation — ends up as the weakest SDG score in 2025. That’s why looking at the countries who managed to climb to the very top feels important. The chart here follows the five standouts: Korea (Rep.), United States, Austria, Israel, and Switzerland.

What you see is not a straight line but years of climb, dips, then sharp recoveries. Korea pulls ahead early, hitting near the ceiling long before others catch up. Switzerland and Austria creep up slower but by the end, they’re right there in the top band. The United States and Israel trail in the first stretch but around the 2010s they kick into gear, speeding up and narrowing the gap.

By 2025 all five are nearly neck-and-neck, pressing close to maximum scores. The story here isn’t only about industrial strength — it’s about who could turn policy, tech, and innovation into lasting momentum. For everyone else still struggling with Goal 9, this timeline shows what that kind of consistent drive can do.

fig = sdg.plot_goal_entity_timeline(
    df_sdg, df_lookup,
    goal="SDG_9",
    entity_type="Country",
    top_n=5, top_mode="top"
)
fig.show()

---

Bottom 5 Countries for Goal 9

Here’s the other side of Goal 9 — the countries left trailing in the race for industrial growth, innovation, and infrastructure. Every nation on this list comes from Africa, which shows how uneven progress can look when global systems tilt against whole regions.

South Sudan sits at the very bottom, barely scoring above zero, a stark reflection of ongoing conflict and fragile state structures. Chad and Madagascar follow close behind, both struggling to move beyond single digits. The Central African Republic edges slightly higher, though still locked in the bottom tier. Somalia closes the list, but its score isn’t much better, just scraping into double digits.

This cluster of nations highlights the imbalance: while the top countries hit near-perfect scores, these five face barriers that go beyond policy — from political instability and conflict to fragile economies and limited infrastructure. Their position at the bottom of Goal 9 reminds us that industrial resilience isn’t just about technology or investment — it’s about stability, governance, and long-term support that many of these countries have been denied.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='SDG_9', group_col='Country', top_n=5, ascending=True, year=2025, plot_theme="plotly_dark")
fig.show()

---

Timeline of the Bottom 5 Countries for Goal 9

This timeline lays bare how the lowest-ranked countries in Goal 9 have fared from 2000 through 2025. The story is less about steady climbs and more about flatlines, slow drifts, and modest spikes.

South Sudan holds almost at rock-bottom through most of the years, creeping only slightly above zero by the end — a sign of how fragile development remains there. Chad, Madagascar, and the Central African Republic track a similar path, inching forward but still barely lifting above single digits. Somalia shows short-lived rises, peaking briefly above 10 before slipping back again, its progress easily disrupted.

What’s striking is not the small upward ticks but the persistent stagnation: these countries move so slowly compared to the leaps made elsewhere. Their position on the chart underlines the structural gap — regions mired in conflict, instability, and economic strain simply don’t have the base to push industrialization forward.

This timeline is a reminder that in global measures like Goal 9, progress is not evenly shared — and without focused support, the distance between the leaders and the laggards only grows wider.

fig = sdg.plot_goal_entity_timeline(
    df_sdg, df_lookup,
    goal="SDG_9",
    entity_type="Country",
    top_n=5, top_mode="bottom"
)
fig.show()

Ranking of Goal 9 Indicators

This chart shows the standing of SDG 9 indicators for the year 2025, breaking down progress across different measures of infrastructure, innovation, and industrial capacity.

• Road access (sdg9_roads) holds the top spot with an average score of 80.65, highlighting transport infrastructure as the strongest aspect of Goal 9.
• Mobile subscriptions (sdg9_mobuse) follows closely at 76.20, pointing to widespread digital connectivity.
• Internet usage (sdg9_intuse) comes third with 68.60, reflecting decent but uneven adoption levels across regions.
• Logistics performance (sdg9_lpi) stands at 53.76, suggesting moderate efficiency in supply chain systems.
• University presence (sdg9_uni) averages 46.64, showing gaps in higher education access and capacity.
• Research articles (sdg9_articles) come in at 41.01, highlighting weaknesses in scientific output.
• Patents (sdg9_patents) and the Research & Development Index (sdg9_rdex) rank lowest, with scores of 21.72 and 21.30, signaling that innovation-driven growth remains a major challenge.

Overall, the data suggests that while physical and digital infrastructure are performing well, innovation-related outputs—research, patents, and R&D—lag significantly, holding back balanced progress under Goal 9.

fig = sdg.plot_goal_ranking(df_sdg, df_lookup, 2025, rank_by="sdg", goal_filter="SDG_9")
fig.show()

Regional Ranking for Goal 9 Indicator : Access to Roads (2025)

The bar chart displays the regional performance for the SDG9 indicator on roads in 2025.
The OECD region leads with the highest score (98.63), showing strong development in road infrastructure.

Following closely, East & South Asia (89.37), LAC (87.65), Eastern Europe & Central Asia (86.61), and MENA (84.06) demonstrate robust outcomes, highlighting continued investment in connectivity.

On the lower end, Africa (55.78) and Oceania (52.00) trail behind, signaling disparities in infrastructure quality and access. Western Europe shows no recorded score in this dataset for 2025.

This comparison illustrates how infrastructure development remains uneven across regions, with a clear divide between high-performing OECD countries and lagging regions like Oceania and Africa.

fig = sdg.plot_sdg_ranking(df_sdg, value_col='IND_9_roads', group_col='Region', top_n=8, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

Timeline of Goal 9 Indicators

This chart follows the performance of all key indicators that make up Goal 9: Industry, Innovation and Infrastructure.
The set includes eight distinct measures:

• sdg9_roads: Coverage and quality of road networks
• sdg9_intuse: Internet usage rates
• sdg9_mobuse: Mobile penetration levels
• sdg9_lpi: Logistics performance index
• sdg9_uni: Tertiary education and university enrolments
• sdg9_articles: Scientific and technical publications
• sdg9_rdex: Research and development expenditure
• sdg9_patents: Registered patents

Patterns show clear divergence. Road infrastructure (sdg9_roads) holds a steady lead across the years, stabilising around the 80-point mark. Meanwhile, mobile usage (sdg9_mobuse) accelerates sharply from the mid-2000s, surpassing many other indicators by 2020. Internet use (sdg9_intuse) and logistics performance (sdg9_lpi) trend upward at a moderate pace, while R&D expenditure (sdg9_rdex) and patent activity (sdg9_patents) remain comparatively stagnant.

This uneven progression underlines the fact that connectivity and infrastructure expansion outpaced innovation capacity. Goal 9’s headline performance is therefore driven more by physical networks and digital access than by research intensity or technological breakthroughs.

goal_ind = (df_lookup.loc[df_lookup["sdg"] == 9, "code"].tolist())
goal_ind = [c for c in goal_ind if not c.startswith("SDG_")]
goal_ind

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, items=goal_ind)
fig.show()

goal_ind = (df_lookup.loc[df_lookup["sdg"] == 9, "code"].tolist())
goal_ind = [c for c in goal_ind if not c.startswith("SDG_")]
goal_ind

['IND_9_roads',
 'IND_9_intuse',
 'IND_9_mobuse',
 'IND_9_lpi',
 'IND_9_uni',
 'IND_9_articles',
 'IND_9_rdex',
 'IND_9_patents',
 'IND_9_rdres',
 'IND_9_netacc',
 'IND_9_womensci']

Timeline of Goal 9 Indicators by Region

The chart shows how different Goal 9 (Industry, Innovation and Infrastructure) indicators have moved from 2000 to 2025 across global regions.

• Transport and Connectivity (sdg9_roads, sdg9_mobuse, sdg9_intuse):
  Road access remains consistently high in many regions, but mobile and internet use indicators reveal steep upward climbs, especially after 2010, signaling rapid adoption of digital technologies.

• Trade and Logistics (sdg9_lpi, sdg9_rdex):
  Logistics performance (LPI) and research export intensity both show modest upward shifts but with wide gaps across regions, hinting at uneven trade and research integration.

• Knowledge and Innovation (sdg9_uni, sdg9_articles, sdg9_patents):
  University enrollments and scientific publications demonstrate growth over time, with OECD and W_Europe leading. Patent activity remains more volatile, reflecting concentrated innovation capacity.

• Overall Picture:
  Goal 9 indicators reveal a mixed but generally upward trend in global infrastructure, technology use, and research capacity. Digital adoption (mobile and internet) appears to be the strongest driver of progress, while innovation-heavy metrics such as patents still lag in many regions.

This timeline highlights that while access to technology is spreading fast, advanced research and innovation capacity remain clustered in developed regions.

fig1 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_roads", entity_type="Region", top_mode="top")
fig2 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_mobuse", entity_type="Region", top_mode="top")
fig3 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_intuse", entity_type="Region", top_mode="top")
fig4 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_lpi", entity_type="Region", top_mode="top")
fig5 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_uni", entity_type="Region", top_mode="top")
fig6 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_articles", entity_type="Region", top_mode="top")
fig7 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_patents", entity_type="Region", top_mode="top")
fig8 = sdg.plot_goal_entity_timeline(df_sdg, df_lookup, goal="IND_9_rdex", entity_type="Region", top_mode="top")

combined = sdg.combine_figs([fig1, fig2, fig3, fig4, fig5, fig6, fig7, fig8], rows=4, width=1000, height=1200, main_title="Timeline of Goal 9 Indicators by Region")
combined.show()

---

Top 5 Positive Correlations with Goal 9: Industry, Innovation and Infrastructure (2025)

These are the strongest positive correlations for Goal 9 in 2025:

• Goal 3 shows the highest positive link (0.86).
• Goal 16 follows closely with 0.81.
• Goal 4 also has a strong positive relationship (0.76).
• Goal 1 and Goal 6 both register solid positive values (0.75 each).

This indicates that progress in infrastructure and innovation strongly aligns with improvements in health, justice, education, poverty reduction, and water access.

df_corr = sdg.correlate_with_target(df_sdg, df_lookup, target="SDG_9", year=2025, top_n=5, sign="pos")
sdg.format_corr_table(df_corr)

### Correlations with SDG_9: Industry, Innovation & Infrastructure

	Code	Description	Correlation	Abs.Correlation
0	SDG_3	Good Health and Well-being	0.86	0.86
1	SDG_16	Peace, Justice & Strong Institutions	0.81	0.81
2	SDG_4	Quality Education	0.76	0.76
3	SDG_1	No Poverty	0.75	0.75
4	SDG_6	Clean Water and Sanitation	0.75	0.75

---

Top 5 Negative Correlations with Goal 9: Industry, Innovation and Infrastructure (2025)

These are the strongest negative correlations for Goal 9 in 2025:

• Goal 12 shows the most negative link (-0.85).
• Goal 13 follows with -0.59.
• Goal 14 has a weaker negative relationship (-0.12).
• Goal 15 is nearly neutral, showing only a very small negative value (0.02).
• Goal 17 is slightly positive (0.24), included here as part of the top 5.

This suggests that while infrastructure and innovation align strongly with social progress, they may sometimes conflict with sustainable consumption, climate, and environmental targets.

df_corr = sdg.correlate_with_target(df_sdg, df_lookup, target="SDG_9", year=2025, top_n=5, sign="neg")
sdg.format_corr_table(df_corr)

### Correlations with SDG_9: Industry, Innovation & Infrastructure

	Code	Description	Correlation	Abs.Correlation
0	SDG_12	Responsible Consumption & Production	-0.85	0.85
1	SDG_13	Climate Action	-0.59	0.59
2	SDG_14	Life Below Water	-0.12	0.12
3	SDG_15	Life on Land	0.02	0.02
4	SDG_17	Partnerships for the Goals	0.24	0.24

fig = sdg.plot_sdg_ranking(df_sdg, value_col='IND_9_roads', group_col='Region', top_n=8, ascending=False, year=2025, plot_theme="plotly_dark")
fig.show()

---

Ranking of SDG Dimensions

The SDG framework is often grouped into four broader dimensions: Environmental, Social, Economic, and Partnership. These dimensions allow us to move beyond single indicators or goals and instead evaluate progress at a higher level. Ranking the dimensions helps reveal which areas of sustainable development are advancing fastest, and which remain behind.

In this section, I rank the SDG dimensions across countries and regions to provide a comparative view of performance. This includes global averages to highlight general trends, as well as breakdowns by region to uncover geographic differences. The rankings make it easier to spot imbalances — for example, regions that may score well economically but struggle environmentally, or those that perform strongly on partnerships but lag in social outcomes.

The purpose of this section is to give a broad perspective before drilling down into individual goals or indicators. By starting at the dimension level, we can see the overall balance of progress across the world and set a context for the more detailed goal-by-goal and indicator-level analyses that follow.

Performance Rankings - by country

df_rank = sdg.get_ranking_table(df_sdg, df_lookup, year=2025, start_year=2015, end_year=2025)
df_rank.head(10)

	Rank (2025)	Country	Region	Score (Overall Score)	Percent Change (2015→2025; Overall Score)
0	1	Finland	OECD	87.02	0.78
1	2	Sweden	OECD	85.74	0.25
2	3	Denmark	OECD	85.26	0.86
3	4	Germany	OECD	83.67	1.45
4	5	France	OECD	83.14	2.12
5	6	Austria	OECD	83.01	-0.81
6	7	Norway	OECD	82.72	0.88
7	8	Croatia	E_Euro_Asia	82.39	2.53
8	9	Poland	OECD	82.08	3.06
9	10	Czechia	OECD	81.94	0.77

Global SDG Percent Change (2015 to 2025)

df_pct, fig = sdg.pct_change_sdg(
            df_sdg=df_sdg, df_lookup=df_lookup,
            start_year=2015, end_year=2025,
            level="goal",
            entity_type=None,
            entities=None,
            sort_desc=False,
            return_fig=True,
        )
fig.show()
df_pct

	code	start	end	delta	pct_change	group	description	label
0	SDG_16	64.87	61.79	-3.08	-4.74	social	Peace, Justice & Strong Institutions	SDG_16
1	SDG_13	84.39	83.88	-0.51	-0.60	environmental	Climate Action	SDG_13
2	SDG_11	74.11	73.80	-0.31	-0.42	social	Sustainable Cities & Communities	SDG_11
3	SDG_2	57.76	58.12	0.36	0.63	social	Zero Hunger	SDG_2
4	SDG_4	74.13	74.69	0.55	0.75	social	Quality Education	SDG_4
5	SDG_12	76.42	77.22	0.79	1.04	economic	Responsible Consumption & Production	SDG_12
6	SDG_6	67.47	69.29	1.81	2.69	environmental	Clean Water and Sanitation	SDG_6
7	SDG_14	62.89	64.65	1.75	2.79	environmental	Life Below Water	SDG_14
8	SDG_8	68.77	71.13	2.36	3.43	economic	Decent Work & Economic Growth	SDG_8
9	SDG_1	72.02	75.30	3.28	4.56	social	No Poverty	SDG_1
10	SDG_17	63.97	66.97	3.00	4.68	partnership	Partnerships for the Goals	SDG_17
11	SDG_15	62.06	65.13	3.07	4.94	environmental	Life on Land	SDG_15
12	SDG_3	67.76	71.61	3.86	5.69	social	Good Health and Well-being	SDG_3
13	SDG_7	59.09	62.73	3.64	6.17	social	Affordable and Clean Energy	SDG_7
14	SDG_10	60.93	66.15	5.22	8.57	economic	Reduced Inequalities	SDG_10
15	SDG_5	56.91	62.33	5.42	9.52	social	Gender Equality	SDG_5
16	SDG_9	40.97	51.53	10.56	25.78	economic	Industry, Innovation & Infrastructure	SDG_9

Quadrant Scatter - 2015 to 2025

fig_scat, tbl_scat = sdg.leaders_laggards_scatter(
        df_sdg=df_sdg, df_lookup=df_lookup,
        start_year=2015, end_year=2025,
        region=None,
        measure=None,
        label_top=5,
        as_3d=True,
        marker_size=5,
        fig_height=500)
fig_scat.show()
tbl_scat.head(10)

	Region	Country	start	end	pct_change	bucket
0	E_S_Asia	Nepal	63.687500	69.019935	8.372813	Emerging
1	E_Euro_Asia	Tajikistan	66.000000	68.583435	3.914296	Emerging
2	LAC	El Salvador	66.294118	68.441274	3.238834	Emerging
3	E_S_Asia	Philippines	63.058824	68.335205	8.367396	Emerging
4	LAC	Paraguay	66.187500	68.327701	3.233543	Emerging
5	MENA	Egypt, Arab Rep.	65.411765	68.091050	4.096029	Emerging
6	LAC	Bolivia	64.812500	68.016598	4.943643	Emerging
7	Africa	Cabo Verde	63.058824	67.279396	6.693073	Emerging
8	E_S_Asia	India	60.529412	66.954602	10.614990	Emerging
9	Africa	Gabon	62.705882	65.578577	4.581220	Emerging

Benchmark Gaps - New Zealand - Gap to top-quartile (OECD)

fig_bench, tbl_bench = sdg.plot_gap_dumbbell_sdg(
    df_sdg=df_sdg,
    year=2025,
    country="New Zealand",
    benchmark="top_quartile",
    label_yshift=0,
    region_benchmark=True,
    plot_height=500,
    line_color="#7393B3"
)
fig_bench.show()

fig = sdg.plot_goal_ranking(df_sdg, df_lookup, year=2025, rank_by="group", ascending=True, fig_height=300)
fig.show()

---

Environmental Goals

The Environmental group achieved the highest ranking among the SDG dimensions in 2025, with a mean score of 70.73. This reflects strong global progress on issues related to climate action, biodiversity, water, and sustainable ecosystems.

The Environmental group is made up of the following goals:

• Goal 6: Ensure availability and sustainable management of water and sanitation for all
• Goal 13: Take urgent action to combat climate change and its impacts
• Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development
• Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, halt and reverse land degradation, and halt biodiversity loss

This strong performance shows that environmental priorities are leading global progress, highlighting the importance of water security, climate action, and ecosystem protection as central pillars of sustainable development.

---

Environmental Goals Timeline

The environmental agenda paints a mixed landscape.
SDG_13 (Climate Action) stays near the top, hovering in the mid 80s, though its curve is more flat than rising.
SDG_6 (Clean Water and Sanitation) demonstrates gradual growth, climbing closer to 70 by 2025.
SDG_14 (Life Below Water) and SDG_15 (Life on Land) rise more slowly, moving from the low 60s into the mid 60s.

Overall, the group shows progress, but the pace remains slower than the urgency of environmental challenges.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, items=None, mode="goal", group_filter="environmental")
fig.show()

---

Environmental Goals Ranking

The figure shows the ranking of SDG goals under the Environmental group for the year 2025.

• Goal 13 (Climate Action) holds the top position with the highest mean score, showing strong performance in climate-related indicators.
• Goal 6 (Clean Water and Sanitation) comes next, reflecting notable improvements in water resource management and access to safe sanitation.
• Goal 15 (Life on Land) and Goal 14 (Life Below Water) follow, both with lower but fairly close scores compared to Goal 6.

Insight
The results highlight that climate action is relatively advanced, while biodiversity-focused goals (land and ocean ecosystems) remain weaker. This suggests that while immediate action on climate change is prioritized, more effort is needed to sustain natural ecosystems.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Environmental", level="goal", ascending=True, fig_height=300)
fig.show()

---

Environmental SDG Indicators Ranking

The chart displays the ranking of SDG indicators that fall under the Environmental group for the year 2025.

• Top indicators include sdg13_co2export and sdg14_biomar, both above 90, showing strong outcomes in climate-related emissions and marine biodiversity.
• Mid-range indicators like sdg6_water, sdg6_scarcew, and sdg15_forchg cluster between 80–85, reflecting steady but uneven progress in freshwater access, water scarcity, and forest change.
• Lower-scoring indicators such as sdg6_wastewat (33.12) and sdg14_cleanwat (38.94) highlight ongoing challenges in wastewater treatment and clean water availability.

Insight
The results suggest that while climate action and biodiversity conservation show clear strengths, water management and pollution control remain weak points. Addressing these gaps will be critical to balance overall environmental sustainability and ensure progress is not lopsided across different ecological dimensions.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Environmental", level="sdg", ascending=True, fig_height=700)
fig.show()

PCA biplot of environmental SDG goals

This plot shows a PCA biplot for environmental goals in the year 2025.
The arrows represent SDG goals and point in the direction where the variance of the data is strongest.
Countries are placed according to their scores, and their position shows how they relate to the goals.

For example, SDG 13 points strongly to the right, which means countries in that area are more aligned with climate action indicators.
SDG 6 points to the left, showing a different variance pattern linked to clean water and sanitation.
SDG 14 and SDG 15 point upward, highlighting the environmental dimensions of life below water and life on land.

Together, the plot helps us see which countries are grouped near certain goals and how the goals themselves diverge from one another in terms of explained variance.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="environmental", kind="biplot", color_by_group=False, biplot_xscale=1.3, biplot_yscale=1.2, line_width=2, as_3d=False)
fig.show()

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="environmental", kind="biplot", color_by_group=False, biplot_xscale=1.3, biplot_yscale=1.2, line_width=2, as_3d=True)
fig.show()

Regional Ranking by Environmental Group

The plot highlights regional performance in environmental sustainability for 2025.
Eastern Europe & Asia and OECD stand out as the top performers, indicating stronger policies and infrastructure for environmental management.
In contrast, MENA and Western Europe show lower scores, reflecting disparities in progress and suggesting areas needing greater focus on sustainability initiatives.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="environmental", group_col="Region", year=2025, top_n=8, df_lookup=df_lookup, fig_height=400)
fig.show()

---

Country Ranking by Environmental Group

The charts display the top- and bottom-performing countries in environmental sustainability for the year 2025.

• Top countries: Hungary and Belarus lead with the strongest scores, reflecting significant progress in environmental initiatives. Central and Eastern European countries, including Czechia, Croatia, and the Slovak Republic, also perform strongly. This suggests a regional emphasis on sustainable practices and environmental resilience.

• Bottom countries: Kuwait, Bahrain, and Qatar from the MENA region rank the lowest, alongside Israel (OECD) and Trinidad and Tobago (LAC). These results indicate that environmental challenges persist in regions with high dependence on fossil fuels, rapid urbanization, and limited diversification in energy sources.

Insight
While Central and Eastern Europe shows notable success in advancing environmental goals, several countries in the MENA region and parts of the Caribbean lag significantly behind. This contrast highlights uneven global progress and the need for stronger international cooperation and targeted policies to address environmental vulnerabilities in lower-performing regions.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="environmental", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, fig_height=300)
fig.show()

fig = sdg.plot_sdg_ranking(df_sdg, value_col="environmental", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, ascending=True, fig_height=300)
fig.show()

---

Environmental Correlations with Other Groups

The Environmental goals show distinct patterns of correlation with Social, Economic, and Partnership goals in 2025:

• Environmental ↔ Social
  Strong positive correlations are observed, especially between Goal 6 (Clean Water and Sanitation) and multiple Social goals such as health, poverty, and education. This highlights how water and sanitation improvements directly reinforce human well-being and equity. However, Goal 13 (Climate Action) shows negative correlations with several Social goals, reflecting potential trade-offs between climate measures and short-term social priorities.

• Environmental ↔ Economic
  Mixed relationships emerge. Goal 6 (Clean Water and Sanitation) again shows positive links with Economic goals, indicating that water security supports economic productivity and infrastructure. In contrast, Goal 13 (Climate Action) displays negative correlations with some Economic goals, suggesting tensions between climate policies and growth/production patterns.

• Environmental ↔ Partnership
  The correlations with Partnership goals (notably Goal 17) are weaker overall, but modest positive links exist with Goal 6, emphasizing how international cooperation can help scale up water and sanitation infrastructure. Other environmental goals show near-neutral or slight negative associations, indicating limited synergy at the global partnership level.

Summary
Environmental priorities strongly align with Social development and moderately with Economic outcomes, but reveal trade-offs around climate action. Partnership links are weaker, underlining the need for stronger global collaboration to fully integrate environmental concerns into broader SDG frameworks.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('environmental','social'),('environmental','economic'),('environmental','partnership')], fig_width=400)
fig.show()

---

Social Goals

The Social group ranked second among the SDG dimensions in 2025, with a mean score of 67.55. This highlights steady progress on human development issues such as health, education, gender equality, and reducing inequalities.

The Social group is made up of the following goals:

• Goal 1: End poverty in all its forms everywhere
• Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture
• Goal 3: Ensure healthy lives and promote well-being for all at all ages
• Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
• Goal 5: Achieve gender equality and empower all women and girls
• Goal 10: Reduce inequality within and among countries
• Goal 11: Make cities and human settlements inclusive, safe, resilient, and sustainable
• Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build effective, accountable, and inclusive institutions

This performance underscores the importance of social priorities as essential pillars of sustainable development, addressing core aspects of poverty, education, health, equality, and justice.

---

Social Goals Timeline

The social cluster carries strong variation.
SDG_11 (Sustainable Cities) and SDG_16 (Peace and Justice) rise steadily into the 70s.
SDG_1 (No Poverty) and SDG_4 (Quality Education) climb strongly, shaping upward arcs.
SDG_2 (Zero Hunger) lags behind, struggling to reach the high 50s.

Together, the group moves forward but with fractures. Education and cities advance, while hunger and justice slow the momentum.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, group_filter="social")
fig.show()

---

Social Goals Ranking

The figure shows the ranking of SDG goals under the Social group for the year 2025.

• Goal 1 (No Poverty), Goal 4 (Quality Education), and Goal 11 (Sustainable Cities and Communities) lead the group, each scoring above 73, indicating strong progress in reducing poverty, improving education, and advancing sustainable urban living.
• Goal 3 (Good Health and Well-being) follows closely, also reflecting relatively strong outcomes in health services and well-being measures.
• Mid-tier performers include Goal 7 (Affordable and Clean Energy), Goal 5 (Gender Equality), and Goal 16 (Peace, Justice, and Strong Institutions), clustered around the low 60s.
• Goal 2 (Zero Hunger) is the weakest performer within the Social group, with a score of 58.11, suggesting persistent global challenges in food security and nutrition.

Insight
The Social dimension shows strength in poverty reduction, education, and urban sustainability. However, hunger, gender equality, and institutional strength remain pressing issues that could slow balanced social development if not addressed effectively.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Social", level="goal", ascending=True, fig_height=400)
fig.show()

---

Social SDG Indicators Ranking

The chart shows the ranking of SDG indicators under the Social group for the year 2025.

• High performers include sdg17_sprofit$ (96.54), sdg3_hiv (94.29), and sdg2_pestexp (94.17). These indicators demonstrate major achievements in reducing disease burden and improving agricultural sustainability.
• Indicators such as sdg13_co2export, sdg14_biomar, and sdg15_forchg also rank highly, linking social development to broader environmental and economic dimensions.
• Mid-range indicators (scores between 70–85) cover health outcomes (e.g., maternal mortality, TB, non-communicable diseases), education (sdg4_literacy, sdg4_secondary), and infrastructure (sdg11_pm25, sdg11_transport). These reflect steady progress but also highlight areas that require continued investment.
• Low performers include sdg7_renewcon (24.92), sdg17_oda (29.31), and sdg16_rsf (36.16). These reveal weaknesses in renewable energy adoption, foreign aid, and certain aspects of justice and security systems.

Insight
The Social group’s indicator performance shows wide variation. Some health and poverty-related areas have advanced strongly, but sustainable energy, justice, and external support mechanisms lag behind. The gap suggests that while human development has improved in many domains, long-term resilience and equity still require major attention.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Social", level="sdg", ascending=True, fig_height=1400)
fig.show()

PCA biplot of social SDG goals

This plot shows a PCA biplot for social goals in the year 2025.
The arrows represent SDG goals and show the strongest directions of variation in the dataset.
Countries are spread based on their scores, which helps us see how they relate to different social priorities.

SDG 5 points sharply upward, highlighting gender equality as a distinct driver of variation.
SDG 1 points downward, separating the dimension of no poverty.
Other goals like SDG 3, 4, 7, 11, and 16 cluster more closely, showing how health, education, energy, sustainable cities, and peace are interlinked.
The spread shows that some countries align strongly with specific goals while others are more balanced across multiple priorities.

Overall, this plot gives a clear view of how social development goals interact and how countries compare across these social dimensions.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="social", kind="biplot", color_by_group=False, biplot_xscale=1.5, biplot_yscale=2.2, line_width=2)
fig.show()

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="social", kind="biplot", color_by_group=False, biplot_xscale=1.5, biplot_yscale=2.2, line_width=2, as_3d=True)
fig.show()

---

Regional Ranking by Social Group

The Social group shows significant regional variation in 2025. OECD countries lead with the highest performance, reflecting strong advancements in areas such as health, education, and equality. Eastern Europe and Central Asia also rank highly, indicating steady progress on social development targets.

Latin America, East & South Asia, and MENA regions occupy middle positions, showing progress but with room for further improvements. Meanwhile, Oceania and Africa trail behind, highlighting persistent social challenges in access to healthcare, education, and social protection systems.

These results emphasize the widening gap between high-income regions and developing regions in achieving social sustainability targets.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="social", group_col="Region", year=2025, top_n=8, df_lookup=df_lookup)
fig.show()

---

Country Ranking by Social Group

In 2025, the Social group is dominated by Nordic countries, reflecting their long-standing commitment to equality, strong welfare systems, and high-quality education and healthcare.

• Top countries: Denmark leads with the highest score, followed closely by Finland and Sweden, with Iceland and Norway also ranking among the top five. This strong performance reinforces the Nordic model as a global benchmark for social sustainability. The consistent success of these countries highlights how robust institutions, inclusive governance, and comprehensive social policies can drive progress across education, health, and equality.

• Bottom countries: At the other end of the spectrum, several African countries, including the Central African Republic, South Sudan, Chad, and Somalia, register the lowest scores. Afghanistan, representing the E_Euro_Asia region, also ranks among the bottom five. These results reveal persistent challenges related to conflict, weak institutions, poverty, and limited access to healthcare and education.

Insight
The contrast between Nordic countries and the weakest performers highlights stark inequalities in social development worldwide. While some regions have achieved advanced social stability and equity, others continue to struggle with fragile governance and systemic barriers. Addressing these disparities remains central to achieving global progress in the Social dimension of the SDGs.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="social", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, fig_height=300)
fig.show()

fig = sdg.plot_sdg_ranking(df_sdg, value_col="social", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, ascending=True, fig_height=300)
fig.show()

Social Correlations with Other Groups

The Social goals display clear interconnections with Environmental, Economic, and Partnership goals in 2025:

• Social ↔ Environmental
  Strong positive links are visible between social development goals (e.g., Goal 1: No Poverty, Goal 3: Good Health, Goal 4: Quality Education) and Goal 6: Clean Water and Sanitation, underlining how basic services directly reinforce social well-being. On the other hand, Goal 13: Climate Action shows negative associations with many social goals, pointing to tensions where climate measures may challenge immediate social priorities.

• Social ↔ Economic
  Several strong positive correlations emerge, particularly between Goal 9: Industry, Innovation, and Infrastructure and key social goals such as poverty reduction, health, and education. However, negative correlations appear with Goal 12: Responsible Consumption and Production, reflecting trade-offs where sustainable production patterns may conflict with growth and social development objectives.

• Social ↔ Partnership
  The links between social goals and Goal 17: Partnerships for the Goals are modest but positive, suggesting that effective global partnerships help strengthen progress on social objectives. While not as strong as the economic or environmental ties, these correlations show the value of international cooperation in sustaining social gains.

Summary
Social goals are strongly reinforced by access to clean water, infrastructure development, and global partnerships. However, trade-offs appear where climate action and sustainable production intersect with social progress, highlighting the complexity of balancing long-term environmental policies with immediate social needs.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('social','environmental'),('social','economic'),('social','partnership')], fig_width=400)
fig.show()

---

Economic Goals

The Economic group ranked fourth among the SDG dimensions in 2025, with a mean score of 66.53. This reflects steady but comparatively lower global progress on issues tied to economic growth, infrastructure, industry, and inequality reduction.

The Economic group is made up of the following goals:

• Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
• Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
• Goal 10: Reduce inequality within and among countries
• Goal 12: Ensure sustainable consumption and production patterns

This ranking highlights that while economic goals are central to sustainable development, challenges remain in balancing growth with sustainability, reducing inequality, and transitioning to responsible production and consumption practices.

---

Economic Goals Timeline

The economic cluster shows sharper contrasts.
SDG_12 (Responsible Consumption and Production) holds the highest ground in the mid to high 70s.
SDG_8 (Decent Work and Growth) and SDG_10 (Reduced Inequality) remain steady in the 60s.
SDG_9 (Industry and Innovation) trails far lower, climbing from the high 20s to about 50 by 2025.

The group is uneven, with clear strengths in sustainable production but gaps in industrial progress.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, group_filter="economic")
fig.show()

---

Economic Goals Ranking

The figure presents the ranking of SDG goals under the Economic group for the year 2025.

• Goal 12 (Responsible Consumption and Production) leads with the highest score of 77.22, highlighting progress toward sustainable production systems and resource efficiency.
• Goal 8 (Decent Work and Economic Growth) follows closely with 71.17, reflecting strong employment and growth outcomes.
• Goal 10 (Reduced Inequalities) ranks mid-level at 66.16, showing progress but also indicating persistent gaps in equitable growth.
• Goal 9 (Industry, Innovation, and Infrastructure) is the weakest in the Economic group, scoring 51.56, suggesting that industrial development and innovation capacity lag behind other economic areas.

Insight
The Economic dimension demonstrates robust performance in responsible consumption and growth-related goals, but weaker results in innovation and infrastructure reveal a critical area for improvement. Without strengthening Goal 9, long-term competitiveness and resilience of economies may remain limited.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Economic", level="goal", ascending=True, fig_height=300)
fig.show()

---

Economic SDG Indicators Ranking

The chart shows the ranking of SDG indicators under the Economic group for the year 2025.

• High-performing indicators include sdg8_impacc (88.21), sdg8_impslav (85.53), and sdg12_pollimp (84.84). These reflect progress in international market access, reduced slavery, and improvements in sustainable production and imports.
• Indicators like sdg12_explastic (81.01), sdg9_roads (80.65), and sdg12_pollprod (79.45) also demonstrate solid advancement in infrastructure, trade, and reducing plastic and pollution impacts.
• Mid-tier indicators such as sdg8_unemp (75.71), sdg10_gini (73.93), and sdg12_ewaste (71.62) highlight moderate success in tackling unemployment, income inequality, and waste management.
• Lower-performing indicators are mainly linked to innovation and research, with sdg9_uni (46.64), sdg9_articles (41.01), sdg9_patents (21.72), and sdg9_rdex (21.30) showing significant gaps in research output and industrial innovation capacity.

Insight
The Economic group exhibits strong results in access, trade, and sustainability indicators, but innovation, research, and industrial competitiveness remain lagging. This gap suggests that while economies are advancing in trade and production, the long-term drive for innovation-led growth requires more targeted investment and policy support.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Economic", level="sdg", ascending=True, fig_height=800)
fig.show()

PCA biplot of economic SDG goals

This plot shows a PCA biplot for economic goals in the year 2025.
The arrows represent SDG goals and show the main directions of variance in the data.
Countries are placed according to their scores, so their positions indicate how they align with the different goals.

SDG 9 points strongly to the right, which highlights the role of industry, innovation, and infrastructure.
SDG 10 points upward, reflecting reduced inequality across countries.
SDG 8 points downward, showing how decent work and economic growth diverge from the other goals.
SDG 12 stretches horizontally, linking to responsible consumption and production.

The spread of countries shows clusters near certain goals, helping us see which economic priorities dominate in different regions.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="economic", kind="biplot", color_by_group=False, biplot_xscale=1, biplot_yscale=1.5, line_width=2)
fig.show()

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", group_filter="economic", kind="biplot", color_by_group=False, biplot_xscale=1, biplot_yscale=1.5, line_width=2, as_3d=True)
fig.show()

---

Regional Ranking by Economic Group

The Economic group shows notable regional differences in 2025. OECD countries lead with the highest performance, reflecting strong economic stability, infrastructure, and institutional capacity. East & South Asia and Eastern Europe & Central Asia follow closely, highlighting their rapid growth and development momentum.

Meanwhile, regions such as Africa and Western Europe appear at the lower end of the ranking, suggesting challenges in balancing economic growth with broader sustainable development priorities.

This distribution highlights how established economies and fast-growing regions are advancing economically, while other regions may need greater investment and policy support to strengthen their economic contributions to the SDGs.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="economic", group_col="Region", year=2025, top_n=8, df_lookup=df_lookup)
fig.show()

---

Country Ranking by Economic Group

In 2025, the top-performing countries in the Economic group are led by Korea (OECD) and Japan, both demonstrating strong industrial bases and advanced technological capacity. Finland, Sweden, and Israel also appear among the leaders, reflecting their well-developed economies, innovation-driven growth, and resilient infrastructures.

• Top countries: Korea, Japan, Finland, Sweden, and Israel showcase high scores, emphasizing the importance of advanced technology, trade competitiveness, and strong institutions in driving sustainable economic progress. Their success underlines how industrialization and innovation-oriented nations dominate the economic dimension of the SDGs.

• Bottom countries: In contrast, several African nations, including Comoros, South Sudan, Lesotho, and Eswatini, score the lowest in the Economic group. Afghanistan, from the E_Euro_Asia region, also appears in the bottom five. These results highlight persistent struggles with weak infrastructure, limited industrialization, and fragile economic systems.

Insight
The Economic dimension shows a sharp divide between advanced economies and those still grappling with basic structural challenges. While high-income countries continue to expand their technological and industrial capabilities, low-income and conflict-affected states face significant barriers. Bridging this divide will require targeted support, investment in infrastructure, and policies that strengthen innovation and sustainable growth in lagging regions.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="economic", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, fig_height=300)
fig.show()

fig = sdg.plot_sdg_ranking(df_sdg, value_col="economic", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup, ascending=True, fig_height=300)
fig.show()

---

Economic Correlations with Other Groups

The Economic goals show diverse relationships with Environmental, Social, and Partnership goals in 2025:

• Economic ↔ Environmental
  Positive synergies are visible between Goal 9: Industry, Innovation, and Infrastructure and environmental goals, particularly climate action. However, Goal 12: Responsible Consumption and Production displays mixed results, sometimes aligning but often in tension with environmental sustainability, reflecting the trade-offs between industrial growth and ecological balance.

• Economic ↔ Social
  Strong positive correlations emerge between Goal 9 and social goals such as poverty reduction, education, and health, suggesting infrastructure and innovation directly boost social progress. In contrast, Goal 12 often shows negative correlations with social outcomes, indicating that sustainable consumption policies may challenge immediate social development needs.

• Economic ↔ Partnership
  The relationships between economic goals and Goal 17: Partnerships for the Goals are generally weak, with only slight positive or neutral associations. This suggests that while partnerships play a role, they are less directly tied to economic progress compared to environmental or social dimensions.

Summary
Economic goals are most strongly linked with infrastructure-driven social improvements and climate-related environmental gains. Yet they also reveal key tensions, especially where sustainable production (Goal 12) intersects with both social and environmental priorities, highlighting the challenge of aligning growth with sustainability.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('economic','environmental'),('economic','social'),('economic','partnership')], fig_width=400)
fig.show()

---

Partnership Goal

The Partnership group ranked third among the SDG dimensions in 2025, with a mean score of 66.96. This reflects moderate progress on strengthening global cooperation and building inclusive systems for sustainable development.

The Partnership group is made up of the following goal:

• Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development

This performance shows that while progress has been made in fostering global partnerships, significant efforts are still needed to enhance financing, technology transfer, trade, and institutional cooperation to support the broader SDG agenda.

---

Partnership Goals Timeline

This single goal rises slowly.
SDG_17 (Partnerships for the Goals) starts near 63 in 2000 and edges toward 67 by 2025.
The growth is modest, but the steady climb points to gradual strengthening of cooperation.

fig = sdg.plot_sdg_timeline(df_sdg, df_lookup, group_filter="partnership")
fig.show()

---

Partnership Goals Ranking

The chart shows the ranking of SDG goals under the Partnership group for the year 2025.

• Goal 17 (Partnerships for the Goals) is the only goal in this group, scoring 66.96. This indicates a moderate performance in areas such as global cooperation, financial support, trade, and capacity-building.

Insight
While partnerships remain essential to advancing all other SDGs, the score suggests there is room for improvement in global collaboration and support mechanisms. Stronger partnerships and international cooperation will be critical for sustaining progress across the environmental, social, and economic dimensions of sustainable development.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Partnership", level="goal", ascending=True, fig_height=200)
fig.show()

---

Partnership SDG Indicators Ranking

The chart shows the ranking of SDG indicators under the Partnership group for the year 2025.

• Top indicators are sdg17_sprofits (96.54) and sdg17_cohaven (89.27), showing strong performance in sustainable profits and coherence among partner countries.
• Mid-level indicators include sdg17_statperf (63.04) and sdg17_multilat (60.13), reflecting steady but not exceptional progress in statistical capacity and multilateral cooperation.
• Lower scores are found in sdg17_govex (51.49), sdg17_govrev (35.78), and especially sdg17_oda (29.31), pointing to limited results in government effectiveness, revenue mobilization, and official development assistance.

Insight
Partnership indicators reveal a dual picture: while profit-related and coherence measures are performing very strongly, global financial support and governance remain weak. This imbalance suggests that while economic partnerships are solid, international aid and institutional effectiveness must be strengthened to ensure balanced global cooperation.

fig = sdg.plot_group_ranking(df_sdg, df_lookup, year=2025, group="Partnership", level="sdg", ascending=True, fig_height=400)
fig.show()

---

Regional Ranking by Partnership Goal

In 2025, the Partnership group shows diverse regional outcomes, highlighting differences in international cooperation, governance, and institutional capacity.

• Top regions: Latin America and the Caribbean (LAC) leads with the highest score (72.66), reflecting progress in building partnerships, trade agreements, and cross-border cooperation. OECD (69.55) and Eastern Europe & Central Asia (69.47) also rank highly, showcasing the benefits of strong institutional frameworks and established international networks.

• Bottom regions: Oceania (60.50) and Africa (62.83) record the lowest scores, followed by East & South Asia (63.64). These results indicate persistent challenges in regional integration, institutional capacity, and effective governance. Limited resources, geographic isolation, and uneven policy coordination contribute to weaker performance.

Insight
The Partnership dimension reveals a clear divide: regions with robust institutions and established global connections perform more strongly, while those with structural limitations and weaker governance lag behind. Strengthening institutional frameworks, fostering regional cooperation, and expanding multilateral support mechanisms will be critical for improving partnership outcomes in underperforming regions.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="partnership", group_col="Region", year=2025, top_n=8, df_lookup=df_lookup)
fig.show()

fig = sdg.plot_sdg_ranking(df_sdg, value_col="partnership", group_col="Region", year=2025, top_n=8, df_lookup=df_lookup, ascending=True)
fig.show()

---

Country Ranking by Parnership Goal

In 2025, the Partnership group rankings highlight Cuba as the clear leader, followed closely by Chile, Uruguay, Norway, and Montenegro.

The results show strong performances from both Latin American and OECD countries, reflecting diverse but effective strategies in advancing global partnerships and institutional cooperation. These nations are setting the pace in areas like governance, international collaboration, and sustainable development partnerships, demonstrating the importance of cross-border cooperation in achieving the SDGs.

fig = sdg.plot_sdg_ranking(df_sdg, value_col="partnership", group_col="Country", year=2025, top_n=5, df_lookup=df_lookup)
fig.show()

---

Partnership Correlations with Other Groups

The Partnership goals, represented mainly by Goal 17: Partnerships for the Goals, show modest connections with other SDG dimensions in 2025:

• Partnership ↔ Environmental
  Partnerships align moderately with environmental priorities, particularly Goal 6 (Clean Water and Sanitation). However, the link with Goal 13 (Climate Action) shows weaker or mixed correlation, highlighting challenges in translating partnerships into direct climate benefits.

• Partnership ↔ Social
  Stronger positive relationships appear between partnerships and social goals such as Goal 1 (No Poverty), Goal 3 (Health), and Goal 4 (Education). This suggests that international cooperation and resource sharing play a critical role in boosting social outcomes.

• Partnership ↔ Economic
  Correlations with economic goals are weaker and more inconsistent. While Goal 9 (Industry, Innovation, and Infrastructure) shows some alignment, the relationship with Goal 12 (Sustainable Consumption and Production) highlights tensions, reflecting the balance between economic growth and sustainable practices.

Summary
Overall, partnerships serve as an enabling mechanism rather than a direct driver of progress. Their influence is strongest in supporting social development, moderate with environmental goals, and less consistent in driving economic outcomes. This underscores the role of partnerships as a foundation for cross-cutting progress rather than as an isolated impact driver.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','environmental'),('partnership','social'),('partnership','economic')], fig_width=400, fig_height=200)
fig.show()

---

Global SDG Correlation Analysis

While rankings highlight where countries and regions stand, they do not explain how the goals interact with one another. The SDGs are interconnected — progress in one area often supports or constrains progress in another. To understand these relationships, it is important to study how the goals and their indicators correlate across the global dataset.

In this section, I calculate and visualize correlations at different levels: between SDG dimensions, across the 17 goals, and down to specific indicators. Heatmaps, dendrograms, and bar charts are used to highlight both positive and negative relationships. For example, strong positive correlations may reveal areas where goals reinforce each other, while negative correlations may point to trade-offs that policymakers need to address.

The purpose of this section is to uncover patterns of synergy and tension among the goals. By analyzing correlations, the project provides insights into where progress is aligned across multiple dimensions, and where it may be uneven or conflicting. These findings help frame the broader discussion about the integrated nature of sustainable development.

---

Correlation Matrix of SDG Scores for 2025

The heatmap above shows how the 17 Sustainable Development Goals (SDGs) relate to each other based on their scores in 2025. Positive correlations are shaded red, while negative ones lean blue. Darker tones mark stronger relationships.

kwargs = dict(
    df_sdg=df_sdg,
    df_lookup=df_lookup,
    level="goal",
    entity_type=None, 
    entities=None,
    group_filter=None,
    year=2025,
    years=None,
    fig_scale=1000,
    min_abs_corr=0.10,
)
fig = sdg.plot_sdg_network(**kwargs)
fig.show()

kwargs = dict(
    df_sdg=df_sdg,
    df_lookup=df_lookup,
    level="goal",
    entity_type=None, 
    entities=None,
    group_filter=None,
    year=2025,
    years=None,
    fig_scale=1000,
    min_abs_corr=0.10,
    as_3d=True,
)
fig = sdg.plot_sdg_network(**kwargs)
fig.show()

corr_df, fig = sdg.corr_by_entity(
    df_sdg, df_lookup,
    level="goal",
    entity_type="Region",
    entities=None,
    mode="within_year",
    year=2025,
    agg="none",
    return_fig=True
)
fig.show()
#print(sdg.generate_nlp_insight(fig))

df_codes = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal", top_n=10)
df_codes

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_9	SDG_3	economic	social	0.86	0.86	9	3	Industry, Innovation & Infrastructure	Good Health and Well-being
1	SDG_9	SDG_12	economic	economic	-0.85	0.85	9	12	Industry, Innovation & Infrastructure	Responsible Consumption & Production
2	SDG_3	SDG_7	social	social	0.84	0.84	3	7	Good Health and Well-being	Affordable and Clean Energy
3	SDG_1	SDG_3	social	social	0.84	0.84	1	3	No Poverty	Good Health and Well-being
4	SDG_3	SDG_4	social	social	0.82	0.82	3	4	Good Health and Well-being	Quality Education
5	SDG_9	SDG_16	economic	social	0.81	0.81	9	16	Industry, Innovation & Infrastructure	Peace, Justice & Strong Institutions
6	SDG_12	SDG_16	economic	social	-0.80	0.80	12	16	Responsible Consumption & Production	Peace, Justice & Strong Institutions
7	SDG_12	SDG_3	economic	social	-0.80	0.80	12	3	Responsible Consumption & Production	Good Health and Well-being
8	SDG_3	SDG_16	social	social	0.80	0.80	3	16	Good Health and Well-being	Peace, Justice & Strong Institutions
9	SDG_6	SDG_3	environmental	social	0.80	0.80	6	3	Clean Water and Sanitation	Good Health and Well-being

Key Observations

• Strong Positive Clusters:
  Goals 1 (No Poverty), 2 (Zero Hunger), 3 (Good Health), 4 (Quality Education), 6 (Clean Water), 7 (Energy), 9 (Industry/Innovation), and 11 (Sustainable Cities) all show high positive correlations with each other (0.7–0.85). This reflects how advances in social and infrastructure goals tend to move together.

• Weak Links:
  Goals 14 (Life Below Water) and 15 (Life on Land) show very low correlations with most others, hovering around 0.0 to 0.2. This suggests environmental protection is often advancing independently of broader development progress.

• Strong Negative Patterns:
  Goal 12 (Responsible Consumption & Production) stands out with negative correlations against many social goals (−0.6 to −0.8). Similarly, Goal 13 (Climate Action) aligns negatively with Goals 1–9, showing that economic and social growth is still tightly coupled with emissions and unsustainable production.

• Goal 16 (Peace, Justice & Institutions):
  Correlates well with education, health, and poverty reduction (0.6–0.8), hinting that governance and justice frameworks create enabling conditions for broader progress.

• Goal 17 (Partnerships):
  Shows weak-to-moderate positive ties (0.2–0.4) with many other goals, reflecting its supportive but indirect nature.

Interpretation
This matrix underscores the tension between development and sustainability. Social and economic progress (Goals 1–9) are moving together, but often at odds with climate and consumption-related goals (12, 13). Environmental goals (14, 15) remain detached, highlighting a gap between human-focused growth and ecological priorities.

In short: The world in 2025 is still balancing on a knife’s edge — excelling in interconnected social progress but struggling to reconcile it with climate and environmental imperatives.

PCA correlation circle of SDG goals

This plot shows a PCA correlation circle for all SDG goals in the year 2025.
Each arrow represents one SDG goal, with its direction showing how it contributes to the main dimensions of variation.
The circle helps us see how closely goals are related and how they cluster by group.

Social goals (green) cluster tightly together on the right, which means they share similar variance patterns.
Environmental goals (orange) spread upward and left, showing distinct directions for climate, ecosystems, and water.
Economic goals (blue) diverge across the lower half, reflecting work, industry, and inequality.
The partnership goal (pink) stands apart, linking differently from the other three groups.

Overall, the circle highlights which goals move together and which ones capture unique dimensions of global development.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", kind="circle", color_by_group=True, biplot_xscale=3, biplot_yscale=3.3, line_width=2)
fig.show()

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", kind="biplot", color_by_group=True, biplot_xscale=3, biplot_yscale=3.3, line_width=2)
fig.show()

---

SDG Goal Clustering

This dendrogram shows the clustering of all SDG goals for 2025 using Ward’s linkage method. The x-axis represents the distance of dissimilarity: goals that merge at lower distances are more similar, while those joining at higher distances are less related.

The colored horizontal lines mark the main clusters formed in the hierarchy. Goals connected by the same color line belong to the same cluster before merging into larger groups. The final blue line indicates the point where all goals are joined into a single tree.

In this general structure, we see a clear environmental block (Goals 12, 13, 14) forming its own cluster, separate from the rest. Social and economic goals such as 3 (health), 6 (water), 11 (cities), and 16 (justice) cluster tightly at low distances, showing strong internal similarity. Education (Goal 4), gender equality (Goal 5), and energy (Goal 7) also group closely.

Overall, the dendrogram highlights the general clustering of SDGs without pre-filtering by dimension. The line colors and the distance scale provide a guide for interpreting which goals naturally align and which stand apart in the overall development landscape.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", method="ward")
fig.show()

Cross-Group SDG Correlations

Cross-Group Correlation Summary – Chord Diagram

This chord diagram shows the average correlations between the four SDG groups (Economic, Environmental, Social, and Partnership) for 2025. Only relationships with an absolute correlation of 0.4 or higher are displayed. The thickness of each link reflects the strength of the correlation between groups.

The strongest connection is observed between Environmental and Social goals, showing that sustainability efforts are closely tied to progress in health, education, equality, and other social outcomes. The Economic group also links strongly with both Environmental and Social, highlighting how growth and development overlap with sustainability and well-being. The Partnership group acts as a connector, maintaining links to Social and Environmental, but with comparatively weaker influence.

Overall, the diagram illustrates how the SDG groups are not isolated but highly interconnected. Environmental and Social progress stand out as strongly reinforcing each other, while Economic and Partnership dimensions play supporting roles in shaping cross-group outcomes.

fig = sdg.grouped_corr_chord(df_sdg, df_lookup, year=2025, mode="goal", agg="mean", min_abs=0.4)
fig.show()

Cross-Group Correlation Summary – Bar Chart

This bar chart shows the average correlations between SDG groups at the goal level for 2025. Each bar represents the mean correlation value for a pair of groups, with only values in the range –0.1 to 0.5 displayed.

The strongest relationship is found between Partnership and Social goals (0.37), suggesting that collaborative initiatives are closely aligned with improvements in health, education, equality, and other social outcomes. Economic and Social goals also show a moderate positive correlation (0.22), highlighting how growth and development intersect with social progress.

Other group pairs — such as Economic ↔ Environmental (0.06), Economic ↔ Partnership (0.09), Environmental ↔ Partnership (0.09), and Environmental ↔ Social (0.07) — display weaker correlations, indicating limited overlap across those dimensions in 2025.

Overall, the results show that partnerships are most effective when tied to social development, while environmental goals remain more loosely connected to the other groups.

fig = sdg.grouped_corr_barchart(df_sdg, df_lookup, year=2025, mode="goal", agg="mean", p_range=[-0.1, 0.5])
fig.show()

df_group_corr = sdg.get_cross_group_corr_sym(df_sdg, df_lookup, year=2025)
df_group_corr

	group_a	group_b	corr_mean	corr_abs_mean	n_pairs	pair
0	economic	social	0.22	0.56	32	economic ↔ social
1	partnership	social	0.37	0.37	8	partnership ↔ social
2	environmental	social	0.07	0.33	32	environmental ↔ social
3	economic	environmental	0.06	0.31	16	economic ↔ environmental
4	economic	partnership	0.09	0.17	4	economic ↔ partnership
5	environmental	partnership	0.09	0.14	4	environmental ↔ partnership

---

Environmental ↔ Social

Cross-Group SDG Correlation

The heatmap illustrates the links between environmental and social goals. Stronger red shades show positive alignments, while blue shades highlight trade-offs. This mix reflects where sustainability efforts support or challenge social outcomes.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('environmental','social')], fig_width=800)
fig.show()

PCA correlation circle of environmental and social SDG goals

This plot shows a PCA correlation circle for environmental and social goals in the year 2025.
Each arrow represents a goal, and its direction shows how it contributes to the main dimensions of variation.
The circle helps us see how the two groups of goals compare to each other.

Environmental goals (orange) point in separate directions, especially SDG 13, 14, and 15, which stand out strongly.
This shows that climate action, life below water, and life on land capture distinct dimensions of variation.
Social goals (green) cluster closely together on the right side, reflecting their shared patterns across education, health, gender, and other social priorities.
Their tight grouping suggests that many social outcomes are interconnected, while environmental outcomes are more diverse and spread apart.

Together, the circle highlights the contrast between the cohesion of social development and the broader spread of environmental challenges.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", kind="circle", group_filter=["environmental","social"], color_by_group=True, biplot_xscale=3, biplot_yscale=3.3, line_width=2)
fig.show()

---

SDG Goal Clustering: Environmental ↔ Social Goals

The dendrogram shows how the environmental and social SDGs are related based on their 2025 scores.

Goals 3, 6, 11, and 16 are tightly clustered, showing strong similarity in outcomes. Education (Goal 4), energy (Goal 7), and gender equality (Goal 5) also group together early, highlighting shared progress patterns in the social space.

On the other hand, the environmental goals — Climate Action (Goal 13), Life Below Water (Goal 14), and Life on Land (Goal 15) — form a separate block. They are internally linked but merge with social goals only at higher distances, meaning their trends are distinct.

Poverty (Goal 1) sits at the intersection, bridging the environmental cluster with the social and economic goals. This suggests that poverty reduction plays a central role in connecting human well-being with sustainability.

Overall, the clustering reveals two major branches: one dominated by social/economic goals and the other by environmental goals. This highlights areas of synergy within each group, while also pointing to possible gaps across them.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["environmental","social"])
fig.show()

Top 5 Positive Correlations: Environmental ↔ Social Goals (2025)

The analysis highlights strong positive correlations between environmental and social goals, showing where progress in one domain reinforces the other. The five strongest positive correlations are:

1. Goal 6 (Clean Water and Sanitation) ↔ Goal 3 (Good Health and Well-being)
   Correlation: 0.80

   • Access to clean water directly improves health outcomes by reducing waterborne diseases and ensuring safe sanitation.

2. Goal 6 (Clean Water and Sanitation) ↔ Goal 7 (Affordable and Clean Energy)
   Correlation: 0.77

   • Expanding sustainable energy systems supports water management, while clean water supply boosts energy efficiency in health and community services.

3. Goal 6 (Clean Water and Sanitation) ↔ Goal 4 (Quality Education)
   Correlation: 0.76

   • Reliable access to clean water allows children, especially girls, to attend school regularly instead of spending time fetching water, improving educational opportunities.

4. Goal 6 (Clean Water and Sanitation) ↔ Goal 1 (No Poverty)
   Correlation: 0.76

   • Water access reduces poverty by improving agricultural productivity, reducing health costs, and enabling sustainable livelihoods.

5. Goal 6 (Clean Water and Sanitation) ↔ Goal 11 (Sustainable Cities and Communities)
   Correlation: 0.75

   • Clean water and sanitation are fundamental for building safe, resilient, and inclusive urban communities.

Interpretation:
These strong positive correlations show that investments in Goal 6 (Clean Water and Sanitation) act as a multiplier, simultaneously advancing health, education, poverty reduction, and sustainable urban growth. It highlights clean water as a cornerstone of both environmental and social progress.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("environmental","social")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_6	SDG_3	environmental	social	0.80	0.80	6	3	Clean Water and Sanitation	Good Health and Well-being
1	SDG_6	SDG_7	environmental	social	0.78	0.78	6	7	Clean Water and Sanitation	Affordable and Clean Energy
2	SDG_6	SDG_4	environmental	social	0.76	0.76	6	4	Clean Water and Sanitation	Quality Education
3	SDG_6	SDG_1	environmental	social	0.76	0.76	6	1	Clean Water and Sanitation	No Poverty
4	SDG_6	SDG_11	environmental	social	0.75	0.75	6	11	Clean Water and Sanitation	Sustainable Cities & Communities

---

Top 5 Negative Correlations: Environmental ↔ Social Goals (2025)

The analysis reveals several negative correlations between environmental and social goals, indicating tensions where progress in one domain may hinder or conflict with the other. The five strongest negative correlations are:

1. Goal 13 (Climate Action) ↔ Goal 3 (Good Health and Well-being)
   Correlation: -0.57

   • Climate action policies, such as rapid industrial transitions or carbon taxes, may initially strain health systems and vulnerable populations.

2. Goal 13 (Climate Action) ↔ Goal 16 (Peace, Justice, and Strong Institutions)
   Correlation: -0.54

   • Climate-related stresses can heighten social instability, challenge governance, and complicate institution-building.

3. Goal 13 (Climate Action) ↔ Goal 1 (No Poverty)
   Correlation: -0.51

   • Aggressive climate mitigation efforts sometimes impose costs that disproportionately affect low-income communities, creating short-term poverty risks.

4. Goal 13 (Climate Action) ↔ Goal 4 (Quality Education)
   Correlation: -0.42

   • Disruptions linked to climate adaptation or resource allocation may reduce investments in education and limit access for children in climate-vulnerable regions.

5. Goal 13 (Climate Action) ↔ Goal 11 (Sustainable Cities and Communities)
   Correlation: -0.39

   • Urban areas undergoing climate adaptation (e.g., relocation, infrastructure overhauls) may face setbacks in inclusivity, affordability, and community stability.

Interpretation:
While Goal 13 (Climate Action) is essential, these negative correlations highlight potential trade-offs with social development. This suggests that climate strategies must be carefully designed with equity safeguards to avoid undermining health, poverty alleviation, education, or urban inclusivity. Aligning climate policy with social protections is crucial to reduce these tensions.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal", pairs=[("environmental","social")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_13	SDG_3	environmental	social	-0.57	0.57	13	3	Climate Action	Good Health and Well-being
1	SDG_13	SDG_16	environmental	social	-0.54	0.54	13	16	Climate Action	Peace, Justice & Strong Institutions
2	SDG_13	SDG_1	environmental	social	-0.51	0.51	13	1	Climate Action	No Poverty
3	SDG_13	SDG_4	environmental	social	-0.42	0.42	13	4	Climate Action	Quality Education
4	SDG_13	SDG_11	environmental	social	-0.39	0.39	13	11	Climate Action	Sustainable Cities & Communities

---

Cross-Group SDG Correlations: Environmental ↔ Social Indicators (2025)

This heatmap shows the correlations between environmental and social SDG indicators for 2025, highlighting where the two dimensions reinforce each other or show potential trade-offs.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('environmental','social')], indicator_mode="sdg", fig_width=1100, fig_height=600)
fig.show()

Top 5 Positive Correlations: Environmental ↔ Social SDG Indicators (2025)

The strongest positive correlations between environmental and social indicators for 2025 are:

1. Access to drinking water (Environmental) ↔ Access to electricity (Social)
   Correlation: 0.91

2. Access to sanitation (Environmental) ↔ Access to clean fuels (Social)
   Correlation: 0.89

3. Access to sanitation (Environmental) ↔ Access to electricity (Social)
   Correlation: 0.88

4. Access to sanitation (Environmental) ↔ Universal health coverage (Social)
   Correlation: 0.88

5. Access to sanitation (Environmental) ↔ Under-5 mortality rate (Social)
   Correlation: 0.86

Interpretation:
These results show that improvements in environmental services like water and sanitation are tightly linked with social outcomes such as health, electricity access, and reduced child mortality.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("environmental","social")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_6_water	IND_7_elecac	environmental	social	0.91	0.91	6	7	Population using at least basic drinking water...	Population with access to electricity (%)
1	IND_6_sanita	IND_7_elecac	environmental	social	0.89	0.89	6	7	Population using at least basic sanitation ser...	Population with access to electricity (%)
2	IND_6_sanita	IND_7_cleanfuel	environmental	social	0.89	0.89	6	7	Population using at least basic sanitation ser...	Population with access to clean fuels and tech...
3	IND_6_sanita	IND_3_uhc	environmental	social	0.88	0.88	6	3	Population using at least basic sanitation ser...	Universal health coverage (UHC) index of servi...
4	IND_6_sanita	IND_3_u5mort	environmental	social	0.86	0.86	6	3	Population using at least basic sanitation ser...	Mortality rate, under-5 (per 1,000 live births)

Top 5 Negative Correlations: Environmental ↔ Social SDG Indicators (2025)

The strongest negative correlations between environmental and social indicators for 2025 are:

1. GHG emissions embodied in imports (Environmental) ↔ Timeliness of administrative proceedings (Social)
   Correlation: -0.78

2. GHG emissions embodied in imports (Environmental) ↔ Corruption Perceptions Index (Social)
   Correlation: -0.77

3. Imported deforestation (Environmental) ↔ Corruption Perceptions Index (Social)
   Correlation: -0.73

4. GHG emissions embodied in imports (Environmental) ↔ Expropriations are lawful and adequately compensated (Social)
   Correlation: -0.71

5. GHG emissions embodied in imports (Environmental) ↔ Life expectancy at birth (Social)
   Correlation: -0.70

Interpretation:
These negative correlations suggest that environmental pressures, particularly emissions and deforestation, are linked with weaker governance, corruption, and lower health outcomes. This indicates areas where environmental degradation and weak social institutions may reinforce each other negatively.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg", pairs=[("environmental","social")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_13_ghgimport	IND_16_admin	environmental	social	-0.78	0.78	13	16	GHG emissions embodied in imports (tCO₂/capita)	Timeliness of administrative proceedings (wors...
1	IND_13_ghgimport	IND_16_cpi	environmental	social	-0.77	0.77	13	16	GHG emissions embodied in imports (tCO₂/capita)	Corruption Perceptions Index (worst 0-100 best)
2	IND_15_impdefor	IND_16_cpi	environmental	social	-0.73	0.73	15	16	Imported deforestation (m²/capita)	Corruption Perceptions Index (worst 0-100 best)
3	IND_13_ghgimport	IND_16_exprop	environmental	social	-0.71	0.71	13	16	GHG emissions embodied in imports (tCO₂/capita)	Expropriations are lawful and adequately compe...
4	IND_13_ghgimport	IND_3_lifee	environmental	social	-0.70	0.70	13	3	GHG emissions embodied in imports (tCO₂/capita)	Life expectancy at birth (years)

---

Social ↔ Economic

This heatmap shows the correlation patterns between social and economic goals.
Red cells highlight positive synergies, while blue cells indicate potential trade-offs.
The mix of colors suggests both reinforcing links (e.g., health and infrastructure) and tensions (e.g., sustainable production vs growth).

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('social','economic')], fig_width=800, fig_height=500)
fig.show()

PCA correlation circle of social and economic SDG goals

This plot shows a PCA correlation circle for social and economic goals in the year 2025.
Each arrow represents a goal, with its length and direction showing how it contributes to the main dimensions of variation.

Social goals (green) cluster together on the right, showing that health, education, equality, and related priorities share common variance.
Economic goals (orange) show a different pattern. SDG 10 points sharply upward, reflecting reduced inequality as a distinct factor.
SDG 12 points to the left, standing apart from the rest, while SDG 8 and 9 align more closely with the social cluster.

Overall, this comparison shows that social outcomes remain tightly linked, while some economic goals diverge strongly, highlighting different development pressures across countries.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", kind="circle", group_filter=["social","economic"], color_by_group=True, biplot_xscale=3, biplot_yscale=3.3, line_width=2)
fig.show()

---

SDG Goal Clustering: Social ↔ Economic Goals (2025)

The dendrogram shows how the social and economic goals cluster together based on their correlations in 2025.
The horizontal axis represents the linkage distance, which indicates how similar or dissimilar the goals are. Goals that merge at smaller distances are more closely related in their patterns, while those that merge at larger distances are less similar.

From the structure, we can see that several social goals (such as Goal 3, Goal 16, and Goal 11) group tightly together, reflecting strong commonalities. Some economic goals (like Goal 8 and Goal 9) appear closer to this cluster, suggesting overlap between economic growth and social well-being. In contrast, goals such as Goal 12 remain more isolated, showing weaker alignment with the others.

This clustering provides an overview of how social and economic dimensions interact, and highlights which goals are strongly interlinked versus those that follow more independent patterns.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["social","economic"])
fig.show()

---

Top 5 Positive Correlations: Social ↔ Economic Goals (2025)

The analysis highlights strong positive correlations between social and economic goals, showing where improvements in social well-being reinforce economic development. The five strongest positive correlations are:

1. Goal 3 (Good Health and Well-being) ↔ Goal 9 (Industry, Innovation, and Infrastructure)
   Correlation: 0.86

   • Investments in resilient infrastructure support health systems through better access to hospitals, clean technology, and innovation in medical services. Healthy populations also contribute to stronger economic productivity.

2. Goal 16 (Peace, Justice, and Strong Institutions) ↔ Goal 9 (Industry, Innovation, and Infrastructure)
   Correlation: 0.81

   • Stable governance and justice systems create an environment where industries can grow, while inclusive infrastructure supports more equitable and peaceful societies.

3. Goal 4 (Quality Education) ↔ Goal 9 (Industry, Innovation, and Infrastructure)
   Correlation: 0.76

   • Education strengthens the workforce and research capacity, while infrastructure development ensures equal access to schools and learning facilities, fostering innovation and skills.

4. Goal 1 (No Poverty) ↔ Goal 9 (Industry, Innovation, and Infrastructure)
   Correlation: 0.75

   • Reducing poverty enables wider participation in industrial and economic growth, while infrastructure investment creates jobs and opportunities that reduce income inequalities.

5. Goal 2 (Zero Hunger) ↔ Goal 9 (Industry, Innovation, and Infrastructure)
   Correlation: 0.75

   • Agricultural innovation and food distribution rely on robust infrastructure, while improved nutrition supports economic productivity and workforce resilience.

Interpretation:
These correlations show that Goal 9 (Industry, Innovation, and Infrastructure) plays a central linking role across the social domain, amplifying progress in health, education, justice, poverty reduction, and hunger eradication. It underscores infrastructure and innovation as key drivers for achieving broader social and economic sustainability.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("social","economic")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_3	SDG_9	social	economic	0.86	0.86	3	9	Good Health and Well-being	Industry, Innovation & Infrastructure
1	SDG_16	SDG_9	social	economic	0.81	0.81	16	9	Peace, Justice & Strong Institutions	Industry, Innovation & Infrastructure
2	SDG_4	SDG_9	social	economic	0.76	0.76	4	9	Quality Education	Industry, Innovation & Infrastructure
3	SDG_1	SDG_9	social	economic	0.75	0.75	1	9	No Poverty	Industry, Innovation & Infrastructure
4	SDG_2	SDG_9	social	economic	0.75	0.75	2	9	Zero Hunger	Industry, Innovation & Infrastructure

---

Top 5 Negative Correlations: Social ↔ Economic Goals (2025)

The analysis highlights strong negative correlations between social and economic goals, showing where progress in one domain may hinder or conflict with the other. The five strongest negative correlations are:

1. Goal 3 (Good Health and Well-being) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.80

   • Improvements in consumption and production efficiency may reduce short-term industrial outputs that support healthcare systems, highlighting potential trade-offs between sustainable consumption and direct health service expansion.

2. Goal 16 (Peace, Justice, and Strong Institutions) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.80

   • Governance and justice reforms may conflict with entrenched economic systems tied to unsustainable production models, creating friction between institutional stability and economic production.

3. Goal 11 (Sustainable Cities and Communities) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.71

   • Efforts to build sustainable cities may initially demand reductions in production-intensive practices, leading to slower industrial economic gains but fostering long-term sustainability.

4. Goal 1 (No Poverty) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.69

   • Poverty reduction often relies on economic expansion, which may conflict with sustainable consumption goals that restrict resource use and industrial growth.

5. Goal 4 (Quality Education) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.66

   • Expanding education systems requires resources and infrastructure that may challenge sustainability targets if reliant on unsustainable consumption and production practices.

Interpretation:
These negative correlations suggest Goal 12 (Responsible Consumption and Production) is a central tension point when aligned with social priorities. While sustainable production is crucial for long-term resilience, it may limit immediate economic growth that supports health, education, poverty reduction, and governance reforms. This highlights the challenge of balancing short-term social progress with long-term environmental responsibility.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal", pairs=[("social","economic")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_3	SDG_12	social	economic	-0.80	0.80	3	12	Good Health and Well-being	Responsible Consumption & Production
1	SDG_16	SDG_12	social	economic	-0.80	0.80	16	12	Peace, Justice & Strong Institutions	Responsible Consumption & Production
2	SDG_11	SDG_12	social	economic	-0.71	0.71	11	12	Sustainable Cities & Communities	Responsible Consumption & Production
3	SDG_1	SDG_12	social	economic	-0.69	0.69	1	12	No Poverty	Responsible Consumption & Production
4	SDG_4	SDG_12	social	economic	-0.66	0.66	4	12	Quality Education	Responsible Consumption & Production

---

Cross-Group SDG Correlations: Social ↔ Economic Indicators (2025)

This heatmap shows the correlations between social and economic SDG indicators.
The visualization highlights both strong positive associations (red) and negative trade-offs (blue), illustrating where improvements in social outcomes align with or conflict against economic indicators.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('social','economic')], indicator_mode="sdg", fig_width=1000, fig_height=1000)
fig.show()

---

Top 5 Positive Correlations: Social ↔ Economic Indicators (2025)

The analysis highlights the strongest positive correlations between social and economic indicators, showing where improvements in human well-being align closely with economic progress.

1. Poverty headcount ratio at $3.65/day (%) ↔ Population using the internet (%)
   Correlation: 0.90
   Broader internet access is strongly associated with reduced poverty, reflecting the role of connectivity in enabling economic participation and opportunity.

2. Universal health coverage (UHC) index of service coverage ↔ Population using the internet (%)
   Correlation: 0.87
   Widespread digital connectivity supports stronger health systems through telemedicine, data access, and service integration.

3. Neonatal mortality rate (per 1,000 live births) ↔ Population using the internet (%)
   Correlation: 0.86
   Improved connectivity links to lower neonatal mortality by facilitating access to maternal health information and medical services.

4. Population with access to clean fuels and technology for cooking (%) ↔ Population using the internet (%)
   Correlation: 0.86
   Clean energy adoption correlates with internet access, both reflecting infrastructure development and modernized living standards.

5. Lower secondary completion rate (%) ↔ Population using the internet (%)
   Correlation: 0.85
   Education outcomes improve alongside digital access, highlighting the internet’s role in expanding learning opportunities.

Interpretation:
These results demonstrate that digital connectivity (internet access) acts as a central driver linking poverty reduction, education, health, and sustainable energy. Progress in internet access appears to cascade into broader social and economic benefits, making it a critical lever for sustainable development.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("social","economic")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_1_lmicpov	IND_9_intuse	social	economic	0.90	0.90	1	9	Poverty headcount ratio at $3.65/day (%)	Population using the internet (%)
1	IND_3_uhc	IND_9_intuse	social	economic	0.88	0.88	3	9	Universal health coverage (UHC) index of servi...	Population using the internet (%)
2	IND_3_neonat	IND_9_intuse	social	economic	0.86	0.86	3	9	Neonatal mortality rate (per 1,000 live births)	Population using the internet (%)
3	IND_7_cleanfuel	IND_9_intuse	social	economic	0.86	0.86	7	9	Population with access to clean fuels and tech...	Population using the internet (%)
4	IND_4_second	IND_9_intuse	social	economic	0.85	0.85	4	9	Lower secondary completion rate (%)	Population using the internet (%)

---

Top 5 Negative Correlations: Social ↔ Economic Indicators (2025)

The analysis identifies strong negative correlations between social and economic indicators, where progress in one area is often associated with setbacks in another. These patterns highlight potential trade-offs that require balanced policy strategies.

1. Corruption Perceptions Index (worst 0–100 best) ↔ Fatal work-related accidents embodied in imports (per 100,000 workers)
   Correlation: -0.78
   Higher corruption levels are linked to more fatal work-related accidents in trade, suggesting weak governance undermines labor safety in economic systems.

2. Timeliness of administrative proceedings (worst 0–100 best) ↔ Fatal work-related accidents embodied in imports (per 100,000 workers)
   Correlation: -0.76
   Delays in administrative processes correlate with unsafe labor practices embedded in global supply chains, signaling inefficiency translates to weaker protections.

3. Corruption Perceptions Index (worst 0–100 best) ↔ Nitrogen emissions associated with imports (kg/capita)
   Correlation: -0.75
   Poor governance correlates with higher import-related nitrogen emissions, showing a governance gap in environmental accountability.

4. Timeliness of administrative proceedings (worst 0–100 best) ↔ Air pollution associated with imports (DALYs per 100,000)
   Correlation: -0.75
   Administrative inefficiency correlates with greater pollution embedded in imports, highlighting trade-offs between governance and environmental health.

5. Timeliness of administrative proceedings (worst 0–100 best) ↔ Nitrogen emissions associated with imports (kg/capita)
   Correlation: -0.74
   Weak administrative performance is linked with higher embedded emissions, underlining governance as a key determinant of environmental sustainability in trade.

Interpretation
These findings reveal that weak governance and corruption (SDG 16 indicators) often correlate negatively with economic trade-related outcomes, including labor safety and environmental performance. This highlights that governance reforms are critical to reducing hidden costs of economic activity in imports and ensuring sustainable development.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg", pairs=[("social","economic")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_16_cpi	IND_8_impacc	social	economic	-0.78	0.78	16	8	Corruption Perceptions Index (worst 0-100 best)	Fatal work-related accidents embodied in impor...
1	IND_16_admin	IND_8_impacc	social	economic	-0.76	0.76	16	8	Timeliness of administrative proceedings (wors...	Fatal work-related accidents embodied in impor...
2	IND_16_cpi	IND_12_nimport	social	economic	-0.75	0.75	16	12	Corruption Perceptions Index (worst 0-100 best)	Nitrogen emissions associated with imports (kg...
3	IND_16_admin	IND_12_pollimp	social	economic	-0.75	0.75	16	12	Timeliness of administrative proceedings (wors...	Air pollution associated with imports (DALYs p...
4	IND_16_admin	IND_12_nimport	social	economic	-0.74	0.74	16	12	Timeliness of administrative proceedings (wors...	Nitrogen emissions associated with imports (kg...

---

Economic ↔ Environmental

This heatmap shows the correlation patterns between environmental and economic goals.
Red cells highlight positive synergies, while red cells indicate potential trade-offs.
The mix of colors suggests that while some areas like clean water and sustainable infrastructure align strongly, other areas (such as climate action vs. production) reveal tensions that may reflect competing priorities between environmental protection and economic growth.

Key observations:

• Positive links (red) imply opportunities where advancing environmental goals (e.g., sanitation, clean water) supports stronger economic outcomes (e.g., infrastructure and innovation).
• Negative links (blue) highlight trade-offs, often where economic expansion (e.g., production and consumption) places stress on environmental sustainability.

This visualization emphasizes the need to balance growth with sustainability, ensuring that economic progress does not undermine long-term ecological stability.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('economic','environmental')], fig_width=800)
fig.show()

PCA correlation circle of economic and environmental SDG goals

This plot shows a PCA correlation circle for economic and environmental goals in the year 2025.
Each arrow represents a goal, and its direction shows how it contributes to the main dimensions of variation.

Environmental goals (green) point upward, with SDG 13, 14, and 15 showing strong and distinct directions linked to climate action, life below water, and life on land.
Economic goals (orange) spread across the lower half of the circle. SDG 9 and 8 point outward, reflecting infrastructure and economic growth, while SDG 12 moves leftward, highlighting responsible consumption and production as a separate factor.
SDG 10 sits closer to the social dimension, but here aligns moderately with the environmental cluster.

Overall, this comparison highlights how environmental challenges are more diverse in their variance patterns, while economic goals are oriented toward growth, production, and inequality, showing both overlap and separation with environmental dimensions.

fig, _ = sdg.plot_sdg_pca(df_sdg, df_lookup, year=2025, variable_type="goal", kind="circle", group_filter=["economic","environmental"], color_by_group=True, biplot_xscale=3, biplot_yscale=3.3, line_width=2)
fig.show()

---

SDG Goal Clustering: Economic ↔ Environmental Goals

The dendrogram shows how the environmental and economic goals cluster together based on their correlations in 2025.
The horizontal axis represents the linkage distance, with smaller distances indicating stronger similarities and larger distances showing weaker connections.

From the clustering, we see that some environmental and economic goals (such as Goal 6 and Goal 8) appear relatively close, reflecting links between clean water, sanitation, and sustainable economic activity. Goal 9 and Goal 10 cluster more broadly with other goals, highlighting their alignment with wider development themes. On the other hand, Goals 12, 13, and 14 remain more separated, indicating weaker similarity with the rest of the group.

This result suggests that while certain environmental goals are closely tied to economic outcomes, others follow distinct paths and highlight the trade-offs or independent priorities within these two dimensions.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["Economic","Environmental"])
fig.show()

---

Top 5 Positive Correlations: Economic ↔ Environmental Goals (2025)

The analysis highlights the strongest positive correlations between environmental and economic goals, showing areas where progress in one dimension directly reinforces the other. The top five positive correlations are:

1. Goal 6 (Clean Water and Sanitation) ↔ Goal 9 (Industry, Innovation and Infrastructure)
   Correlation: 0.75

   • Expanding access to water and sanitation underpins resilient infrastructure, enabling industries and communities to thrive.

2. Goal 13 (Climate Action) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: 0.74

   • Climate action strategies align with sustainable production practices, driving more responsible resource use.

3. Goal 6 (Clean Water and Sanitation) ↔ Goal 8 (Decent Work and Economic Growth)
   Correlation: 0.56

   • Access to safe water improves health and productivity, directly boosting labor capacity and economic stability.

4. Goal 6 (Clean Water and Sanitation) ↔ Goal 10 (Reduced Inequalities)
   Correlation: 0.33

   • Reliable water access helps reduce inequality by improving living conditions across vulnerable groups.

5. Goal 15 (Life on Land) ↔ Goal 8 (Decent Work and Economic Growth)
   Correlation: 0.26

   • Protecting ecosystems fosters sustainable livelihoods through agriculture, forestry, and tourism.

Interpretation
These correlations indicate that water security, climate action, and ecosystem protection serve as critical enablers of economic growth. Investing in these environmental areas not only supports ecological sustainability but also strengthens industries, employment, and infrastructure resilience.
The findings emphasize that sustainable development policies should leverage environmental actions as multipliers for long-term economic progress.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("economic","environmental")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_9	SDG_6	economic	environmental	0.75	0.75	9	6	Industry, Innovation & Infrastructure	Clean Water and Sanitation
1	SDG_12	SDG_13	economic	environmental	0.74	0.74	12	13	Responsible Consumption & Production	Climate Action
2	SDG_8	SDG_6	economic	environmental	0.56	0.56	8	6	Decent Work & Economic Growth	Clean Water and Sanitation
3	SDG_10	SDG_6	economic	environmental	0.33	0.33	10	6	Reduced Inequalities	Clean Water and Sanitation
4	SDG_8	SDG_15	economic	environmental	0.26	0.26	8	15	Decent Work & Economic Growth	Life on Land

---

Top 5 Negative Correlations: Environmental ↔ Economic Goals (2025)

The analysis identifies strong negative correlations between environmental and economic goals, suggesting potential trade-offs where progress in one area could hinder the other. The five strongest negative correlations are:

1. Goal 6 (Clean Water and Sanitation) ↔ Goal 12 (Responsible Consumption and Production)
   Correlation: -0.66

   • Improvements in water and sanitation access may demand higher consumption of materials and energy, creating pressure on sustainable production systems.

2. Goal 13 (Climate Action) ↔ Goal 9 (Industry, Innovation and Infrastructure)
   Correlation: -0.59

   • Climate-focused restrictions on emissions and resource use can conflict with industrial expansion and infrastructure growth.

3. Goal 13 (Climate Action) ↔ Goal 10 (Reduced Inequalities)
   Correlation: -0.38

   • Climate policies might impose costs that disproportionately affect lower-income groups, potentially widening inequality if not mitigated.

4. Goal 13 (Climate Action) ↔ Goal 8 (Decent Work and Economic Growth)
   Correlation: -0.17

   • Transitioning away from carbon-intensive industries can limit short-term job creation and economic expansion.

5. Goal 14 (Life Below Water) ↔ Goal 9 (Industry, Innovation and Infrastructure)
   Correlation: -0.12

   • Marine conservation and strict regulations on resource extraction can restrict industrial activities near coastal zones.

Interpretation These findings reveal areas of tension between environmental protection and economic development. Policies aimed at advancing climate action, clean water, and marine ecosystems may inadvertently slow down industrial expansion, infrastructure projects, or growth in jobs.
The results highlight the need for balanced policy frameworks that align environmental safeguards with inclusive economic strategies to avoid undermining long-term sustainability.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal", pairs=[("environmental","economic")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_6	SDG_12	environmental	economic	-0.66	0.66	6	12	Clean Water and Sanitation	Responsible Consumption & Production
1	SDG_13	SDG_9	environmental	economic	-0.59	0.59	13	9	Climate Action	Industry, Innovation & Infrastructure
2	SDG_13	SDG_10	environmental	economic	-0.38	0.38	13	10	Climate Action	Reduced Inequalities
3	SDG_13	SDG_8	environmental	economic	-0.17	0.17	13	8	Climate Action	Decent Work & Economic Growth
4	SDG_14	SDG_9	environmental	economic	-0.12	0.12	14	9	Life Below Water	Industry, Innovation & Infrastructure

---

Cross-Group SDG Correlations: Economic ↔ Environmental Indicators (2025)

This heatmap illustrates correlations between environmental and economic indicators.
The results show both reinforcing synergies (e.g., sustainable water and energy use supporting economic development) and trade-offs (e.g., higher emissions linked with economic growth indicators).

The mixed color distribution highlights areas where environmental sustainability and economic progress are aligned, as well as regions where conflicts between the two dimensions remain.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('economic','environmental')], indicator_mode="sdg", fig_width=1000, fig_height=600)
fig.show()

Top 5 Positive Correlations: Economic ↔ Environmental SDG Indicators (2025)

The strongest positive correlations between environmental and economic indicators reveal where sustainability outcomes and economic performance are closely aligned. The top five are:

1. SDG13 (GHG emissions embodied in imports) ↔ SDG12 (Nitrogen emissions associated with imports)
   Correlation: 0.97

   • Both indicators capture environmental costs embedded in trade, reflecting how emissions and resource pressures move together with economic imports.

2. SDG13 (GHG emissions embodied in imports) ↔ SDG12 (Air pollution associated with imports, DALYs per capita)
   Correlation: 0.95

   • Emissions linked to imports strongly align with air pollution burdens, showing that international trade carries shared environmental and health impacts.

3. SDG13 (GHG emissions embodied in imports) ↔ SDG8 (Victims of modern slavery embodied in imports)
   Correlation: 0.92

   • Highlights how economic imports can simultaneously carry environmental and social costs, linking carbon footprints with exploitative labor practices.

4. SDG13 (GHG emissions embodied in imports) ↔ SDG8 (Fatal work-related accidents embodied in imports)
   Correlation: 0.90

   • Reinforces the intersection of environmental pressures and unsafe economic production practices embedded in global supply chains.

5. SDG6 (Population using at least basic sanitation services) ↔ SDG9 (Population using the internet)
   Correlation: 0.87

   • Infrastructure and access-based indicators show parallel progress, suggesting that improvements in sanitation align with digital and economic development.

Interpretation:
These results show that global imports and trade act as a nexus for environmental and economic linkages. High correlations between emissions, pollution, and social-economic costs embedded in imports underscore how sustainability challenges cut across multiple SDGs.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("economic","environmental")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_12_nimport	IND_13_ghgimport	economic	environmental	0.97	0.97	12	13	Nitrogen emissions associated with imports (kg...	GHG emissions embodied in imports (tCO₂/capita)
1	IND_12_pollimp	IND_13_ghgimport	economic	environmental	0.95	0.95	12	13	Air pollution associated with imports (DALYs p...	GHG emissions embodied in imports (tCO₂/capita)
2	IND_8_impslav	IND_13_ghgimport	economic	environmental	0.92	0.92	8	13	Victims of modern slavery embodied in imports ...	GHG emissions embodied in imports (tCO₂/capita)
3	IND_8_impacc	IND_13_ghgimport	economic	environmental	0.91	0.91	8	13	Fatal work-related accidents embodied in impor...	GHG emissions embodied in imports (tCO₂/capita)
4	IND_9_intuse	IND_6_water	economic	environmental	0.87	0.87	9	6	Population using the internet (%)	Population using at least basic drinking water...

---

Top 5 Negative Correlations: Economic ↔ Environmental SDG Indicators (2025)

The strongest negative correlations highlight trade-offs between environmental sustainability and economic performance. The top five are:

1. SDG13 (GHG emissions embodied in imports) ↔ SDG9 (Articles published in academic journals)
   Correlation: -0.78

   • Higher carbon emissions in imports are linked with lower research productivity, suggesting a tension between carbon-intensive trade and knowledge-driven economies.

2. SDG6 (Anthropogenic wastewater that receives treatment) ↔ SDG12 (Nitrogen emissions associated with imports)
   Correlation: -0.72

   • While wastewater treatment improves, nitrogen emissions from imports rise, revealing mismatches in environmental protection efforts.

3. SDG13 (GHG emissions embodied in imports) ↔ SDG9 (Logistics Performance Index)
   Correlation: -0.72

   • Efficient logistics correlate with lower emissions in imports, implying that better infrastructure reduces the carbon burden of trade.

4. SDG6 (Anthropogenic wastewater that receives treatment) ↔ SDG8 (Fatal work-related accidents embodied in imports)
   Correlation: -0.71

   • Improvements in water treatment do not align with reductions in unsafe labor practices in global supply chains, highlighting divergent progress.

5. SDG6 (Population using at least basic sanitation services) ↔ SDG12 (E-waste not recollected)
   Correlation: -0.70

   • Gains in sanitation access are associated with rising electronic waste mismanagement, showing sustainability gaps between social and environmental outcomes.

Interpretation:
These negative correlations reveal tensions where economic and social advancements are not aligned with environmental progress. They emphasize the need for integrated policies to avoid situations where gains in one area (e.g., trade, sanitation, logistics) worsen outcomes in another (e.g., emissions, waste).

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg", pairs=[("economic","environmental")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_9_articles	IND_13_ghgimport	economic	environmental	-0.78	0.78	9	13	Articles published in academic journals (per 1...	GHG emissions embodied in imports (tCO₂/capita)
1	IND_12_nimport	IND_6_wastewat	economic	environmental	-0.72	0.72	12	6	Nitrogen emissions associated with imports (kg...	Anthropogenic wastewater that receives treatme...
2	IND_9_lpi	IND_13_ghgimport	economic	environmental	-0.72	0.72	9	13	Logistics Performance Index: Infrastructure Sc...	GHG emissions embodied in imports (tCO₂/capita)
3	IND_8_impacc	IND_6_wastewat	economic	environmental	-0.71	0.71	8	6	Fatal work-related accidents embodied in impor...	Anthropogenic wastewater that receives treatme...
4	IND_12_ewaste	IND_6_sanita	economic	environmental	-0.71	0.71	12	6	Electronic waste that is not recollected (kg/c...	Population using at least basic sanitation ser...

---

Environmental ↔ Partnership

This heatmap shows the correlation patterns between partnership and environmental goals.
The colors indicate the strength and direction of the relationships: red cells highlight positive synergies, while blue cells would show negative trade-offs (none appear strongly here).

Key observations:

• The correlation between Goal 17 (Partnerships for the Goals) and Goal 6 (Clean Water and Sanitation) is notably positive, suggesting that stronger global cooperation supports progress in environmental services and access.
• Links with other environmental goals (Goals 13, 14, and 15) appear weaker, showing limited direct alignment between partnership initiatives and environmental priorities in this dataset.
• The overall pattern suggests that partnerships have more measurable influence on foundational services like water and sanitation, but their role in broader environmental issues such as climate action or biodiversity is less direct.

This visualization highlights how partnerships play a supportive role for specific environmental targets, but also shows the need for stronger integration of collaborative frameworks with climate and ecosystem-focused agendas.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','environmental')], fig_height=300, fig_width=900)
fig.show()

---

SDG Goal Clustering: Environmental ↔ Partnership Goals

The dendrogram illustrates the clustering relationships between environmental and partnership goals in 2025.
The horizontal axis represents linkage distance, where shorter distances indicate stronger similarities and longer distances indicate weaker connections.

From the clustering, we can see that Goal 6 (Clean Water and Sanitation) aligns closely with Goal 17 (Partnerships for the Goals), suggesting that partnerships play a key role in advancing water and sanitation initiatives. Goal 15 (Life on Land) also clusters with this pair, indicating a shared connection between biodiversity, natural resource management, and cooperative efforts. In contrast, Goal 13 (Climate Action) and Goal 14 (Life Below Water) appear more distant, showing weaker alignment with partnership-driven outcomes.

This result highlights that partnerships tend to strongly support foundational environmental services and land-based sustainability, while their impact on climate and ocean-related goals may be less direct.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["environmental","partnership"])
fig.show()

Correlations: Environmental ↔ Partnership Goals

The analysis highlights the strongest positive correlations between environmental and partnership goals, showing how global cooperation contributes to sustainability. The top five positive correlations are:

1. Goal 6 (Clean Water and Sanitation) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.41
   Expanding access to clean water and sanitation benefits from strong international partnerships, reinforcing infrastructure, resource management, and equitable access.

2. Goal 15 (Life on Land) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.07
   Partnerships contribute to conservation efforts, promoting sustainable land use, forestry, and biodiversity protection.

3. Goal 14 (Life Below Water) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: -0.01
   While the relationship is weak, partnerships provide frameworks for cooperation on marine conservation, though their measurable influence remains limited.

4. Goal 13 (Climate Action) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: -0.09
   This negative correlation suggests that global partnerships are not yet strongly aligned with climate mitigation outcomes, reflecting gaps between commitments and implementation.

(Only four environmental goals are mapped against partnerships, hence fewer strong positive relationships compared to other group pairings.)

Interpretation

These results show that partnerships have the most measurable impact when linked to foundational environmental services such as water and sanitation, with weaker or inconsistent alignment across climate, land, and ocean-related goals. While partnerships are essential for mobilizing resources and fostering international collaboration, the relatively low correlations suggest that more targeted, action-oriented partnerships are needed to translate global commitments into direct environmental progress.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("environmental","partnership")], sort_by="corr", ascending=False, top_n=5)
fig

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_6	SDG_17	environmental	partnership	0.41	0.41	6	17	Clean Water and Sanitation	Partnerships for the Goals
1	SDG_15	SDG_17	environmental	partnership	0.06	0.06	15	17	Life on Land	Partnerships for the Goals
2	SDG_14	SDG_17	environmental	partnership	-0.01	0.01	14	17	Life Below Water	Partnerships for the Goals
3	SDG_13	SDG_17	environmental	partnership	-0.09	0.09	13	17	Climate Action	Partnerships for the Goals

Cross-Group SDG Correlations: Environmental ↔ Partnership Indicators

This heatmap illustrates correlations between environmental and partnership indicators.
The results show areas where global cooperation directly reinforces environmental progress (positive synergies) as well as areas where partnerships have weaker or even conflicting alignment with environmental outcomes.

Key observations:

• Positive correlations (red cells) appear between partnership indicators such as governance effectiveness (sdg17_govex) and statistical capacity (sdg17_statperf) with environmental targets like clean water (sdg6_water) and sanitation (sdg6_sanita). This suggests that effective governance and reliable data systems play a strong role in supporting sustainable resource management.
• Mixed patterns are seen with climate-related indicators (sdg13), where partnerships in multilateral agreements (sdg17_multilat) and official development assistance (sdg17_oda) show varying levels of alignment, reflecting both collaboration opportunities and implementation gaps.
• Negative correlations (blue cells) are visible in areas linking partnerships with environmental stress factors such as marine ecosystems (sdg14) and biodiversity (sdg15). These patterns highlight the challenge of translating global cooperation into direct progress on ecological resilience.

The overall distribution of colors shows that while partnerships provide structural support through governance, finance, and cooperation, their measurable influence on climate action, oceans, and biodiversity remains inconsistent. This suggests the need for more targeted, action-oriented partnerships to strengthen alignment with key environmental priorities.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','environmental')], indicator_mode="sdg", fig_width=1000, fig_height=500)
fig.show()

Top 5 Positive Correlations: Environmental ↔ Partnership SDG Indicators

The strongest positive correlations between environmental and partnership indicators reveal how cooperation capacity, governance, and data systems contribute to sustainability progress. The top five positive correlations are:

1. SDG6 (Wastewater treatment) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.60
   Effective wastewater treatment aligns with countries that maintain stronger statistical systems, suggesting that reliable governance frameworks support environmental infrastructure.

2. SDG6 (Access to drinking water) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.58
   Expanding access to clean drinking water is positively linked with the strength of national statistical capacity, reflecting the role of data-driven governance in achieving water security.

3. SDG13 (GHG emissions embodied in imports) ↔ SDG17 (Corporate Tax Haven Score)
   Correlation: 0.58
   Higher GHG emissions linked to imports correlate with weaker corporate tax accountability, showing the global connections between trade, emissions, and governance mechanisms.

4. SDG6 (Access to sanitation) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.56
   Sanitation improvements track closely with strong statistical performance, again emphasizing how governance and monitoring capacity enhance environmental outcomes.

5. SDG6 (Access to sanitation) ↔ SDG17 (Government spending on health and education)
   Correlation: 0.54
   Progress in sanitation aligns with higher public investments in social infrastructure, showing that health and education spending indirectly reinforces environmental service delivery.

Interpretation

These correlations highlight that governance quality, statistical performance, and financial commitments are strongly linked to progress in water, sanitation, and climate-related indicators. Partnerships contribute most effectively where robust institutions and reliable data systems underpin environmental policy. However, the links between emissions and governance show that cooperative frameworks must also address trade-related externalities, ensuring that global cooperation promotes both accountability and sustainability.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("environmental","partnership")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_6_wastewat	IND_17_statperf	environmental	partnership	0.60	0.60	6	17	Anthropogenic wastewater that receives treatme...	Statistical Performance Index (worst 0-100 best)
1	IND_6_water	IND_17_statperf	environmental	partnership	0.58	0.58	6	17	Population using at least basic drinking water...	Statistical Performance Index (worst 0-100 best)
2	IND_13_ghgimport	IND_17_cohaven	environmental	partnership	0.58	0.58	13	17	GHG emissions embodied in imports (tCO₂/capita)	Corporate Tax Haven Score (best 0-100 worst)
3	IND_6_sanita	IND_17_statperf	environmental	partnership	0.56	0.56	6	17	Population using at least basic sanitation ser...	Statistical Performance Index (worst 0-100 best)
4	IND_6_sanita	IND_17_govex	environmental	partnership	0.54	0.54	6	17	Population using at least basic sanitation ser...	Government spending on health and education (%...

Top 5 Negative Correlations: Environmental ↔ Partnership SDG Indicators

The strongest negative correlations between environmental and partnership indicators highlight areas where cooperation mechanisms and environmental outcomes are misaligned. The top five negative correlations are:

1. SDG15 (Imported deforestation per capita) ↔ SDG17 (Official Development Assistance)
   Correlation: -0.55
   High deforestation embedded in imports is negatively linked with development assistance, suggesting that aid flows do not necessarily mitigate environmentally harmful trade practices.

2. SDG13 (GHG emissions embodied in imports) ↔ SDG17 (Official Development Assistance)
   Correlation: -0.48
   Greenhouse gas emissions linked to imports correlate negatively with aid flows, pointing to a disconnect between international financing and the environmental costs of trade.

3. SDG13 (GHG emissions embodied in imports) ↔ SDG17 (Statistical Performance Index)
   Correlation: -0.47
   Stronger statistical systems appear negatively associated with emissions embodied in imports, suggesting that improved data capacity may highlight or constrain high-emission trade flows.

4. SDG6 (Wastewater treatment) ↔ SDG17 (Corporate Tax Haven Score)
   Correlation: -0.45
   Wastewater treatment progress shows negative alignment with corporate tax governance, reflecting how financial secrecy jurisdictions may undermine investment in sustainable infrastructure.

5. SDG13 (GHG emissions embodied in imports) ↔ SDG17 (Government revenue excluding grants)
   Correlation: -0.45
   Emissions embedded in imports correlate negatively with government revenue, indicating that fiscal structures in some countries may not adequately capture or address the costs of carbon-intensive trade.

Interpretation

These results highlight the tensions between international cooperation mechanisms and environmental sustainability. Development assistance and fiscal governance structures show weak or negative links with deforestation, emissions, and wastewater management, underscoring gaps in how partnerships address environmental externalities. Strengthening partnerships to directly target trade-related deforestation, imported emissions, and pollution is crucial for aligning global cooperation with ecological priorities.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("environmental","partnership")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_15_impdefor	IND_17_oda	environmental	partnership	-0.55	0.55	15	17	Imported deforestation (m²/capita)	For high-income and all OECD DAC countries: In...
1	IND_13_ghgimport	IND_17_oda	environmental	partnership	-0.48	0.48	13	17	GHG emissions embodied in imports (tCO₂/capita)	For high-income and all OECD DAC countries: In...
2	IND_13_ghgimport	IND_17_statperf	environmental	partnership	-0.47	0.47	13	17	GHG emissions embodied in imports (tCO₂/capita)	Statistical Performance Index (worst 0-100 best)
3	IND_13_ghgimport	IND_17_govrev	environmental	partnership	-0.46	0.46	13	17	GHG emissions embodied in imports (tCO₂/capita)	Other countries: Government revenue excluding ...
4	IND_6_wastewat	IND_17_cohaven	environmental	partnership	-0.45	0.45	6	17	Anthropogenic wastewater that receives treatme...	Corporate Tax Haven Score (best 0-100 worst)

---

Social ↔ Partnership

This heatmap shows the correlation patterns between partnership and social goals in 2025.
The shades of red indicate positive relationships, where stronger partnerships appear to support social progress across multiple dimensions.

Key observations:

• Goal 17 (Partnerships for the Goals) shows consistently positive correlations with core social goals, such as poverty reduction (Goal 1), hunger reduction (Goal 2), and health (Goal 3).
• Education (Goal 4), gender equality (Goal 5), and sustainable cities (Goal 11) also align positively with partnerships, reflecting the role of global cooperation in advancing inclusive social development.
• The uniform red across all social goals suggests that partnerships act as a broad enabler rather than showing strong selectivity for certain goals.

This visualization highlights the importance of partnerships as a cross-cutting driver that underpins progress across the social dimension, strengthening the foundation for equity, inclusion, and well-being.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','social')], fig_height=300, fig_width=900)
fig.show()

---

SDG Goal Clustering: Social ↔ Partnership Goals

The dendrogram illustrates how social goals cluster with the partnership goal in 2025.
The horizontal axis shows the linkage distance, where closer merges represent stronger similarities in their patterns.

From the structure, we observe that Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice, and Strong Institutions) cluster very closely, followed by Goal 11 (Sustainable Cities and Communities). This indicates strong shared relationships across these social dimensions. Goals 2, 5, 4, and 7 also align into a mid-range cluster, reflecting their interconnectedness in reducing hunger, advancing education, gender equality, and access to energy.

Goal 17 (Partnerships for the Goals) positions itself nearer to social priorities but not within the tightest sub-cluster, suggesting that partnerships play a supporting role across many social issues rather than binding strongly to one in particular. Goal 1 (No Poverty) appears the most distant in the hierarchy, showing weaker similarity compared to other social outcomes.

This clustering highlights that partnerships broadly support social development, with closer alignment to health, governance, and community-oriented goals, while poverty reduction requires more targeted or distinct forms of cooperation.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["social","partnership"])
fig.show()

Correlations: Social ↔ Partnership Goals

The analysis highlights the strongest positive correlations between social and partnership goals, showing how collaboration supports inclusive development. The top five positive correlations are:

1. Goal 1 (No Poverty) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.45
   Partnerships strengthen poverty reduction efforts by enabling resource mobilization, financial support, and international cooperation to reach vulnerable groups.

2. Goal 3 (Good Health and Well-being) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.38
   Partnerships improve access to healthcare services, vaccines, and medical innovation through shared knowledge and coordinated action.

3. Goal 7 (Affordable and Clean Energy) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.38
   Partnerships expand access to sustainable energy, leveraging technology transfer and investment in clean energy solutions.

4. Goal 5 (Gender Equality) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.38
   Partnerships promote gender equality by fostering global advocacy, supporting women’s empowerment initiatives, and enabling inclusive policy reforms.

5. Goal 11 (Sustainable Cities and Communities) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.38
   Partnerships strengthen sustainable urban development through shared investment in infrastructure, housing, and resilient community planning.

Interpretation

These correlations highlight that partnerships act as broad enablers of social development, supporting poverty reduction, health, energy, gender equality, and urban sustainability. The consistent positive values suggest that cooperative frameworks are essential for advancing social well-being, with partnerships serving as catalysts for mobilizing resources and aligning efforts across diverse social priorities.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("social","partnership")], sort_by="corr", ascending=False, top_n=8)
fig

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_1	SDG_17	social	partnership	0.45	0.45	1	17	No Poverty	Partnerships for the Goals
1	SDG_7	SDG_17	social	partnership	0.39	0.39	7	17	Affordable and Clean Energy	Partnerships for the Goals
2	SDG_4	SDG_17	social	partnership	0.38	0.38	4	17	Quality Education	Partnerships for the Goals
3	SDG_3	SDG_17	social	partnership	0.38	0.38	3	17	Good Health and Well-being	Partnerships for the Goals
4	SDG_11	SDG_17	social	partnership	0.38	0.38	11	17	Sustainable Cities & Communities	Partnerships for the Goals
5	SDG_5	SDG_17	social	partnership	0.38	0.38	5	17	Gender Equality	Partnerships for the Goals
6	SDG_16	SDG_17	social	partnership	0.34	0.34	16	17	Peace, Justice & Strong Institutions	Partnerships for the Goals
7	SDG_2	SDG_17	social	partnership	0.27	0.27	2	17	Zero Hunger	Partnerships for the Goals

Cross-Group SDG Correlations: Social ↔ Partnership Indicators

This heatmap shows correlations between partnership indicators and social indicators.
The colors reveal where cooperation mechanisms (governance, financing, data capacity, multilateralism) align with social outcomes.

Key observations

• Positive links appear between governance effectiveness and statistical capacity (sdg17_govex, sdg17_statperf) and many core social outcomes such as health, education, energy access, and urban services. Strong institutions and good data tend to move with better social results.
• Official development assistance and multilateral participation (sdg17_oda, sdg17_multilat) show mixed patterns: they correlate positively with some education and health measures, but less so where progress is uneven or aid is crisis-oriented.
• Negative or weak correlations are more common with conflict, violence, and justice indicators (Goal 16 proxies). Instability undermines the effectiveness of partnerships and blunts improvements across social services.

Interpretation

Overall, partnership capacity—especially governance quality and data systems—tracks with stronger social performance. Financing and multilateral engagement help, but their impact varies by context. Where insecurity and weak institutions persist, partnerships translate less cleanly into gains, highlighting the need to couple cooperation with governance and peace-building reforms.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','social')], indicator_mode="sdg", fig_width=1100, fig_height=400)
fig.show()

Top 5 Positive Correlations: Social ↔ Partnership SDG Indicators

The strongest positive correlations between social and partnership indicators reveal how governance quality, financial commitments, and cooperation mechanisms reinforce social outcomes. The top five positive correlations are:

1. SDG2 (Minimum dietary diversity among children) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.74
   Improved dietary diversity for children is strongly associated with higher statistical capacity, suggesting that robust data systems enable better monitoring and delivery of nutrition programs.

2. SDG1 (Poverty headcount ratio at $3.65/day) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.71
   Poverty reduction outcomes align closely with stronger national data systems, reflecting how governance and reliable statistics support targeted social policies.

3. SDG3 (Neonatal mortality rate) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.69
   Lower neonatal mortality correlates with higher statistical performance, highlighting the role of data-driven governance in strengthening health systems.

4. SDG3 (Universal health coverage index) ↔ SDG17 (Government spending on health and education)
   Correlation: 0.69
   Expanding universal health coverage is linked to greater public investment in health and education, emphasizing the importance of financial commitments for achieving social progress.

5. SDG16 (Expropriations are lawful and adequately compensated) ↔ SDG17 (Official Development Assistance)
   Correlation: 0.68
   Stronger legal protections and governance structures align with higher aid commitments, showing how international cooperation supports justice and institutional integrity.

Interpretation

These correlations demonstrate that governance capacity and public financing play a critical role in advancing social outcomes. Strong statistical systems are particularly influential, underpinning improvements in nutrition, poverty reduction, and health. Partnerships, through both domestic spending and international aid, act as enablers of social resilience, reinforcing the need for reliable data, governance, and investment to sustain long-term progress.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("social","partnership")], sort_by="corr", ascending=False, top_n=5)
fig

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_2_mdd	IND_17_statperf	social	partnership	0.74	0.74	2	17	Minimum dietary diversity among children aged ...	Statistical Performance Index (worst 0-100 best)
1	IND_1_lmicpov	IND_17_statperf	social	partnership	0.71	0.71	1	17	Poverty headcount ratio at $3.65/day (%)	Statistical Performance Index (worst 0-100 best)
2	IND_3_neonat	IND_17_statperf	social	partnership	0.69	0.69	3	17	Neonatal mortality rate (per 1,000 live births)	Statistical Performance Index (worst 0-100 best)
3	IND_3_uhc	IND_17_govex	social	partnership	0.69	0.69	3	17	Universal health coverage (UHC) index of servi...	Government spending on health and education (%...
4	IND_16_exprop	IND_17_oda	social	partnership	0.68	0.68	16	17	Expropriations are lawful and adequately compe...	For high-income and all OECD DAC countries: In...

Top 5 Negative Correlations: Social ↔ Partnership SDG Indicators

The strongest negative correlations between social and partnership indicators highlight areas where governance, financing, or cooperation mechanisms are misaligned with social outcomes. The top five negative correlations are:

1. SDG2 (Human Trophic Level) ↔ SDG17 (Government spending on health and education)
   Correlation: -0.58
   Higher trophic levels (diets dominated by resource-intensive food) correlate negatively with government social spending, reflecting inefficiencies in translating investment into sustainable nutrition outcomes.

2. SDG2 (Human Trophic Level) ↔ SDG17 (Statistical Performance Index)
   Correlation: -0.58
   Dietary sustainability is negatively linked with stronger statistical systems, showing a mismatch between governance capacity and the ability to reduce resource-intensive consumption patterns.

3. SDG2 (Obesity prevalence) ↔ SDG17 (Government revenue excluding grants)
   Correlation: -0.50
   Rising obesity levels correlate negatively with government revenue structures, suggesting fiscal policies may not adequately support healthier social outcomes.

4. SDG16 (Exports of major conventional weapons) ↔ SDG17 (Statistical Performance Index)
   Correlation: -0.47
   Higher arms exports are negatively aligned with statistical system strength, highlighting tensions between governance priorities and peace-building indicators.

5. SDG2 (Minimum dietary diversity among children) ↔ SDG17 (Corporate Tax Haven Score)
   Correlation: -0.46
   Child nutrition diversity shows negative alignment with weak corporate tax accountability, indicating governance gaps that undermine equitable social investment.

Interpretation

These results reveal persistent tensions between governance frameworks and critical social outcomes. Dietary sustainability and health indicators show negative correlations with both statistical systems and fiscal structures, pointing to gaps in how partnerships address food security and public health. Similarly, the link between arms exports and weak statistical performance underscores the need for partnerships to prioritize peace and governance reforms as foundations for social progress.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("social","partnership")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_2_trophic	IND_17_statperf	social	partnership	-0.59	0.59	2	17	Human Trophic Level (best 2-3 worst)	Statistical Performance Index (worst 0-100 best)
1	IND_2_trophic	IND_17_govex	social	partnership	-0.58	0.58	2	17	Human Trophic Level (best 2-3 worst)	Government spending on health and education (%...
2	IND_2_obesity	IND_17_govrev	social	partnership	-0.51	0.51	2	17	Prevalence of obesity, BMI ≥ 30 (% of adult po...	Other countries: Government revenue excluding ...
3	IND_16_weaponsexp	IND_17_statperf	social	partnership	-0.47	0.47	16	17	Exports of major conventional weapons (TIV con...	Statistical Performance Index (worst 0-100 best)
4	IND_2_mdd	IND_17_cohaven	social	partnership	-0.46	0.46	2	17	Minimum dietary diversity among children aged ...	Corporate Tax Haven Score (best 0-100 worst)

---

Economic ↔ Partnership

This heatmap shows the correlation patterns between partnership and economic goals in 2025.
The shades represent the strength of the relationship, with red cells reflecting positive associations and lighter or blueish tones suggesting weaker or negative alignment.

Key observations:

• Goal 17 (Partnerships for the Goals) shows generally positive links with economic goals such as decent work and growth (Goal 8) and industry, innovation, and infrastructure (Goal 9). This reflects the role of partnerships in supporting inclusive economic development and innovation.
• The correlation with Goal 10 (Reduced Inequalities) is neutral to slightly positive, showing that partnerships contribute to addressing inequality but with less direct influence compared to other goals.
• The relationship with Goal 12 (Responsible Consumption and Production) appears weaker, even slightly negative, suggesting that economic expansion supported by partnerships may not always align with sustainable consumption patterns.

This visualization indicates that partnerships reinforce economic growth and infrastructure, but their impact on sustainable production and reduced inequalities may require more targeted collaboration.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','economic')], fig_height=300, fig_width=900)
fig.show()

---

SDG Goal Clustering: Economic ↔ Partnership Goals

The dendrogram illustrates the clustering relationships between economic and partnership goals in 2025.
The horizontal axis represents linkage distance, where shorter distances indicate stronger similarities and longer distances indicate weaker connections.

From the clustering, we can see that Goal 17 (Partnerships for the Goals) aligns most closely with Goal 8 (Decent Work and Economic Growth), reflecting the important role of global cooperation in expanding employment opportunities and supporting sustainable economic growth. This pair also links with Goal 12 (Responsible Consumption and Production), though at a greater distance, indicating a weaker but still present connection between partnerships and sustainable production practices.

Goal 9 (Industry, Innovation, and Infrastructure) and Goal 10 (Reduced Inequalities) cluster further away, suggesting that while partnerships contribute to industrial development and equity, their patterns of alignment are less direct compared to economic growth and production goals.

This result highlights that partnerships are most strongly tied to employment and growth but need more targeted integration with infrastructure, inequality reduction, and sustainable consumption objectives.

fig = sdg.sdg_dendrogram(df_sdg, df_lookup, year=2025, mode="goal", groups=["partnership","economic"])
fig.show()

Correlations: Economic ↔ Partnership Goals (2025)

The analysis highlights the strongest positive correlations between economic and partnership goals, showing how collaboration supports inclusive and sustainable growth. The top correlations are:

1. Goal 8 (Decent Work and Economic Growth) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.28
   Partnerships play a role in promoting decent work by mobilizing financial resources, fostering trade agreements, and encouraging global economic cooperation.

2. Goal 9 (Industry, Innovation, and Infrastructure) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: 0.24
   Collaborative partnerships strengthen industrial development and infrastructure resilience through technology transfer, investment, and global knowledge-sharing.

3. Goal 10 (Reduced Inequalities) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: -0.00
   Partnerships show little measurable influence on reducing inequalities, suggesting gaps in how cooperative frameworks translate into equitable outcomes.

4. Goal 12 (Responsible Consumption and Production) ↔ Goal 17 (Partnerships for the Goals)
   Correlation: -0.16
   The slightly negative value indicates potential trade-offs, where global economic partnerships may encourage production growth that is not always aligned with sustainable consumption practices.

Interpretation

These results suggest that partnerships provide meaningful support to economic growth and infrastructure development but show weaker or even negative alignment with sustainability-oriented goals such as responsible production and reducing inequalities. This points to the need for partnerships to be more strategically aligned with sustainability agendas, ensuring that economic expansion does not come at the expense of equitable or responsible practices.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="goal",pairs=[("economic","partnership")], sort_by="corr", ascending=False, top_n=5)
fig

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	SDG_8	SDG_17	economic	partnership	0.28	0.28	8	17	Decent Work & Economic Growth	Partnerships for the Goals
1	SDG_9	SDG_17	economic	partnership	0.24	0.24	9	17	Industry, Innovation & Infrastructure	Partnerships for the Goals
2	SDG_10	SDG_17	economic	partnership	-0.00	0.00	10	17	Reduced Inequalities	Partnerships for the Goals
3	SDG_12	SDG_17	economic	partnership	-0.16	0.16	12	17	Responsible Consumption & Production	Partnerships for the Goals

Cross-Group SDG Correlations: Economic ↔ Partnership Indicators

This heatmap shows correlations between partnership indicators and economic indicators.
The colors reveal where cooperation mechanisms (governance, financing, data capacity, multilateralism) align with or diverge from economic performance.

Key observations

• Positive correlations (red cells) are most visible between governance quality and statistical capacity (sdg17_govex, sdg17_statperf) and several economic measures such as employment, trade performance, and infrastructure (sdg8_unemp, sdg9_roads, sdg9_lpi). This indicates that effective governance and strong data systems foster healthier economic outcomes.
• Official development assistance (sdg17_oda) and multilateral cooperation (sdg17_multilat) show mixed impacts. They correlate positively with inclusive growth metrics (e.g., sdg8_adjgrowth) but weakly or negatively with production and consumption metrics (e.g., sdg12).
• Negative correlations (blue cells) emerge where partnerships connect to unsustainable consumption and production (sdg12 indicators), reflecting tensions between global cooperation for growth and the need for responsible production systems.

Interpretation

Overall, partnerships are most strongly aligned with enabling inclusive economic growth, trade, and infrastructure through governance and data systems. However, the weaker or negative correlations with responsible production and inequality indicators point to a need for partnerships to be more strategically focused on sustainability and equity, ensuring that cooperative efforts do not simply reinforce growth but also address long-term balance.

fig = sdg.grouped_corr_heatmaps(df_sdg, df_lookup, year=2025, group_pairs=[('partnership','economic')], indicator_mode="sdg", fig_width=1000, fig_height=400)
fig.show()

Top 5 Positive Correlations: Economic ↔ Partnership SDG Indicators

The strongest positive correlations between economic and partnership indicators show how governance capacity, statistical systems, and cooperation mechanisms reinforce economic development. The top five positive correlations are:

1. SDG8 (Adults with an account at a bank or other financial institution) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.73
   Financial inclusion is strongly linked with better statistical performance, highlighting how governance and reliable data systems support equitable access to financial services.

2. SDG9 (Articles published in academic journals) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.68
   Scientific and academic output correlates with strong statistical systems, reflecting the role of research and knowledge as enablers of innovation and global cooperation.

3. SDG9 (Logistics Performance Index) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.68
   Infrastructure and logistics efficiency align with stronger statistical capacity, showing how effective governance supports trade and transport systems.

4. SDG9 (Rural population with access to all-season roads) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.68
   Expanding rural road access correlates with reliable data systems, demonstrating how governance quality underpins equitable infrastructure development.

5. SDG9 (Mobile broadband subscriptions per 100 population) ↔ SDG17 (Statistical Performance Index)
   Correlation: 0.67
   Growth in digital connectivity tracks with strong governance and statistical capacity, suggesting that partnerships strengthen both digital and economic inclusion.

Interpretation

These correlations reveal that partnerships—particularly through governance quality and statistical performance—play a key enabling role in advancing economic outcomes. Financial inclusion, infrastructure, digital access, and research capacity all show strong alignment with partnership indicators. This underscores the importance of building robust governance and data systems as a foundation for sustainable and inclusive economic development.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("economic","partnership")], sort_by="corr", ascending=False, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_8_accounts	IND_17_statperf	economic	partnership	0.73	0.73	8	17	Adults with an account at a bank or other fina...	Statistical Performance Index (worst 0-100 best)
1	IND_9_articles	IND_17_statperf	economic	partnership	0.69	0.69	9	17	Articles published in academic journals (per 1...	Statistical Performance Index (worst 0-100 best)
2	IND_9_roads	IND_17_statperf	economic	partnership	0.68	0.68	9	17	Rural population with access to all-season roa...	Statistical Performance Index (worst 0-100 best)
3	IND_9_lpi	IND_17_statperf	economic	partnership	0.68	0.68	9	17	Logistics Performance Index: Infrastructure Sc...	Statistical Performance Index (worst 0-100 best)
4	IND_9_mobuse	IND_17_statperf	economic	partnership	0.67	0.67	9	17	Mobile broadband subscriptions (per 100 popula...	Statistical Performance Index (worst 0-100 best)

Top 5 Negative Correlations: Economic ↔ Partnership SDG Indicators

The strongest negative correlations between economic and partnership indicators highlight areas where cooperation mechanisms and economic outcomes are misaligned. The top five negative correlations are:

1. SDG12 (Exports of plastic waste per capita) ↔ SDG17 (Statistical Performance Index)
   Correlation: -0.59
   High plastic waste exports are negatively linked with stronger statistical systems, suggesting that better governance does not necessarily translate into sustainable waste management practices.

2. SDG12 (Exports of plastic waste per capita) ↔ SDG17 (Government spending on health and education)
   Correlation: -0.57
   Waste exports correlate negatively with public spending, pointing to a disconnect between fiscal priorities and the regulation of unsustainable production and trade.

3. SDG12 (Municipal solid waste per capita/day) ↔ SDG17 (Government spending on health and education)
   Correlation: -0.56
   Higher solid waste generation is negatively aligned with health and education investment, indicating that fiscal resources are not adequately directed to address environmental burdens of waste.

4. SDG12 (Municipal solid waste per capita/day) ↔ SDG17 (Statistical Performance Index)
   Correlation: -0.54
   Waste management indicators are negatively correlated with statistical performance, showing that governance capacity does not consistently drive reductions in waste intensity.

5. SDG8 (Fatal work-related accidents embodied in imports) ↔ SDG17 (Official Development Assistance)
   Correlation: -0.53
   Work-related accidents embedded in trade flows are negatively correlated with aid flows, highlighting gaps in how partnerships address occupational safety in global supply chains.

Interpretation

These findings emphasize persistent trade-offs between economic growth patterns and global cooperation. High levels of waste generation and trade-linked environmental costs show negative alignment with governance quality and fiscal spending. Similarly, occupational risks embodied in imports remain weakly addressed through partnerships. This underscores the need for partnerships to better integrate sustainability and labor safety standards into trade and economic cooperation frameworks.

fig = sdg.get_grouppair_code_corr(df_sdg, df_lookup, year=2025, mode="sdg",pairs=[("economic","partnership")], sort_by="corr", ascending=True, top_n=5)
fig.head()

	code_a	code_b	group_a	group_b	corr	abs_corr	sdg_a	sdg_b	desc_a	desc_b
0	IND_12_explastic	IND_17_statperf	economic	partnership	-0.59	0.59	12	17	Exports of plastic waste (kg/capita)	Statistical Performance Index (worst 0-100 best)
1	IND_12_explastic	IND_17_govex	economic	partnership	-0.57	0.57	12	17	Exports of plastic waste (kg/capita)	Government spending on health and education (%...
2	IND_12_msw	IND_17_govex	economic	partnership	-0.56	0.56	12	17	Municipal solid waste (kg/capita/day)	Government spending on health and education (%...
3	IND_12_msw	IND_17_statperf	economic	partnership	-0.54	0.54	12	17	Municipal solid waste (kg/capita/day)	Statistical Performance Index (worst 0-100 best)
4	IND_8_impacc	IND_17_oda	economic	partnership	-0.53	0.53	8	17	Fatal work-related accidents embodied in impor...	For high-income and all OECD DAC countries: In...

SDG Projections

df_forecast = sdg.forecast_sdg_any(
    df_sdg=df_sdg,
    df_lookup=df_lookup,
    sdg=None,
    overall=True,
    entity_level="Country",
    entities="New Zealand",
    horizon_to=2030
)
fig = sdg.plot_forecast_from_results(df_forecast, df_sdg, df_lookup)
fig.show()

fig = sdg.plot_forecast_from_results(df_forecast, df_sdg, df_lookup, as_3d=True)
fig.show()

df_forecast = sdg.forecast_sdg_any(
    df_sdg=df_sdg,
    df_lookup=df_lookup,
    sdg=None,
    overall=True,
    entity_level="Region",
    entities="Oceania",
    horizon_to=2030
)
fig = sdg.plot_forecast_from_results(df_forecast, df_sdg, df_lookup)
fig.show()